Cited by 123Bayesian Modeling and Causal InferenceExplainable Artificial Intelligence (XAI)Gaussian Processes and Bayesian Inference

The DOI matches its Crossref record; 164 of the 171 references it cites were found in a registry or an index.

Bayesian networks for interpretable machine learning and optimization

Open access

Mihaljević, Bojan, Bielza, Concha, Larrañaga, Pedro·2021·Neurocomputing

DOI 10.1016/j.neucom.2021.01.138

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1016/j.neucom.2021.01.138

Cites

164 of 164 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The text we found for this paper looks like another document, so the self-check wasn’t run.

Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty.
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810 · cited 1×
“…probabilistic reasoning, implemented with BNs, can be at odds with human reasoning under uncertainty, which is plagued with heuristics and biases [35], formal logic is not necessarily a good model of human reasoning either, as it provides domain-independent rules while human reasoning is…”
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Genetic Programming: On the Programming of Computers by Means of Natural Selection
Koza, J. R. (1992) · Medical Entomology and Zoology · Cited by 13,082
Untitled work
(1999) · Cited by 12,854
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 8,354
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · cited 1×
“Government agencies [4] and other actors [5] are now increasingly demanding for artiﬁcial intelligence to be explainable and transparent; the European Union, for example, guarantees the right to an explanation of an automated decision in domains such as medicine, law and ﬁnance.”
Background
Untitled work
(2009) · Cited by 6,153
Tabu Search
Glover, F., & Laguna, M. (1997) · Cited by 5,679
The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
Bayesian network classifiers
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
Probability theory the logic of science
Jaynes, E. T., & Bretthorst, G. L. (2003) · Cited by 4,600
Machine Learning in Medicine
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579
Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems
Lauritzen, S. L., & Spiegelhalter, D. J. (1988) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1988.tb01721.x · Cited by 3,962
Fast Effective Rule Induction
Cohen, W. W. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50023-2 · Cited by 3,890
Probabilistic Reasoning in Intelligent Systems
(1988) · Elsevier eBooks · doi:10.1016/c2009-0-27609-4 · Cited by 3,371
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121
Mortality in Mental Disorders and Global Disease Burden Implications: A Systematic Review and Meta-analysis
Walker, E. R., McGee, R. E., & Druss, B. G. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.2502 · Cited by 2,976
Interneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954
Artificial Intelligence through Simulated Evolution
Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966) · International Linear Collider · Cited by 2,814
Model of autism: increased ratio of excitation/inhibition in key neural systems
Rubenstein, J. L. R., & Merzenich, M. M. (2003) · Genes, Brain and Behavior · doi:10.1034/j.1601-183x.2003.00037.x · Cited by 2,768
Approximating discrete probability distributions with dependence trees
Chow, C., & Liu, C. (1968) · IEEE Transactions on Information Theory · doi:10.1109/tit.1968.1054142 · Cited by 2,630
Dynamic bayesian networks: representation, inference and learning
Murphy, K. P., & Russell, S. (2002) · Cited by 2,579
A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524
Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain
Azevedo, F. A. C., Carvalho, L. R. B., Grinberg, L. T., Farfel, J. M., Ferretti, R. E. L., Leite, R. E. P., Filho, W. J., Lent, R., & Herculano‐Houzel, S. (2009) · Journal of Comparative Neurology · doi:10.1002/cne.21974 · Cited by 2,337
Inference to the Best Explanation
Lipton, P. (2003) · Routledge eBooks · doi:10.4324/9780203470855 · Cited by 2,293
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
Goodman, B., & Flaxman, S. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2741 · Cited by 2,131 · cited 1×
“Government agencies [4] and other actors [5] are now increasingly demanding for artiﬁcial intelligence to be explainable and transparent; the European Union, for example, guarantees the right to an explanation of an automated decision in domains such as medicine, law and ﬁnance.”
Background
A review of multiobjective test problems and a scalable test problem toolkit
Huband, S., Hingston, P., Barone, L., & While, L. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.861417 · Cited by 2,129
GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits
Tremblay, R., Lee, S., & Rudy, B. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.033 · Cited by 2,033
The computational complexity of probabilistic inference using bayesian belief networks
Cooper, G. F. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90060-d · Cited by 1,979
Steps toward Artificial Intelligence
Minsky, M. (1961) · Proceedings of the IRE · doi:10.1109/jrproc.1961.287775 · Cited by 1,796
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Sachs, K., Perez, O. D., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005) · Science · doi:10.1126/science.1105809 · Cited by 1,785
Can we open the black box of AI?
Castelvecchi, D. (2016) · Nature · doi:10.1038/538020a · Cited by 1,773
Networks of the Brain: Quantitative Analysis and Modeling
Sporns, O. (2010) · The MIT Press eBooks · doi:10.7551/mitpress/8476.001.0001 · Cited by 1,750
Adult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714
Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project
(1986) · Telematics and Informatics · doi:10.1016/s0736-5853(86)80073-9 · Cited by 1,712
The economic cost of brain disorders in Europe
Olesen, J., Gustavsson, A., Svensson, M., Wittchen, H., & Jönsson, B. (2012) · European Journal of Neurology · doi:10.1111/j.1468-1331.2011.03590.x · Cited by 1,560
Reconstruction and Simulation of Neocortical Microcircuitry
Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., et al. (2015) · Cell · doi:10.1016/j.cell.2015.09.029 · Cited by 1,557 · cited 1×
“…a systematic taxonomy from data in the near future [116–118], by clustering [119,120] molecular, morphological, and electrophysiological features, researchers currently use [e.g.,][121] and refer to established morphological types such as chandelier, Martinotti, neurogliaform, and basket [122–125].”
Background
A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics
Schäfer, J., & Strimmer, K. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1175 · Cited by 1,556
Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
The Petilla Interneuron Nomenclature Group (PING) (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2402 · Cited by 1,491
Monetary Costs of Dementia in the United States
Hurd, M. D., Martorell, P., Delavande, A., Mullen, K. J., & Langa, K. M. (2013) · New England Journal of Medicine · doi:10.1056/nejmsa1204629 · Cited by 1,484
Learning Bayesian Networks
Sammut, C., & Webb, G. I. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_100245 · Cited by 1,438
Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann-Holzboog-Verlag. Stuttgart 1973. Broschiert
Vent, W. (1975) · Feddes Repertorium · doi:10.1002/fedr.4910860506 · Cited by 1,333
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Baluja, S. (1994) · Cited by 1,309
Rule-based expert systems: The mycin experiments of the stanford heuristic programming project
Swartout, W. (1985) · Artificial Intelligence · doi:10.1016/0004-3702(85)90067-0 · Cited by 1,248
From recombination of genes to the estimation of distributions I. Binary parameters
Mühlenbein, H., & Paaß, G. (1996) · Lecture notes in computer science · doi:10.1007/3-540-61723-x_982 · Cited by 1,219
Complexity of Finding Embeddings in a k -Tree
Arnborg, S., Corneil, D. G., & Proskurowski, A. (1987) · SIAM Journal on Algebraic and Discrete Methods · doi:10.1137/0608024 · Cited by 1,212
A model for reasoning about persistence and causation
Dean, T., & Kanazawa, K. (1989) · Computational Intelligence · doi:10.1111/j.1467-8640.1989.tb00324.x · Cited by 999
The compact genetic algorithm
Harik, G. R., Lobo, F. G., & Goldberg, D. E. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.797971 · Cited by 986
The pyramidal neuron of the cerebral cortex: Morphological and chemical characteristics of the synaptic inputs
DeFelipe, J., & Fariñas, I. (1992) · Progress in Neurobiology · doi:10.1016/0301-0082(92)90015-7 · Cited by 975
An Algorithm for Fast Recovery of Sparse Causal Graphs
Spirtes, P., & Glymour, C. (1991) · Social Science Computer Review · doi:10.1177/089443939100900106 · Cited by 956
BOA: the Bayesian optimization algorithm
Pelikán, M., Goldberg, D. E., & Cantú‐Paz, E. (1999) · Cited by 943
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Goldberg, D. E. (2002) · Cited by 912
Neuronal cell-type classification: challenges, opportunities and the path forward
Zeng, H., & Sanes, J. R. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.85 · Cited by 841
New insights into the classification and nomenclature of cortical GABAergic interneurons
DeFelipe, J., López-Cruz, P. L., Benavides-Piccione, R., Bielza, C., Larrañaga, P., Anderson, S., Burkhalter, A., Cauli, B., Fairén, A., Feldmeyer, D., et al. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3444 · Cited by 833
Self-citation
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Dagum, P., & Luby, M. G. (1993) · Artificial Intelligence · doi:10.1016/0004-3702(93)90036-b · Cited by 771
A Survey of Optimization by Building and Using Probabilistic Models
Pelikán, M., Goldberg, D. E., & Lobo, F. G. (2002) · Computational Optimization and Applications · doi:10.1023/a:1013500812258 · Cited by 761
Molecular and Physiological Diversity of Cortical Nonpyramidal Cells
Cauli, B., Audinat, E., Lambolez, B., Angulo, M. C., Ropert, N., Tsuzuki, K., Hestrin, S., & Rossier, J. (1997) · Journal of Neuroscience · doi:10.1523/jneurosci.17-10-03894.1997 · Cited by 738
Classifier Technology and the Illusion of Progress
Hand, D. J. (2006) · Statistical Science · doi:10.1214/088342306000000060 · Cited by 723
PageRank Beyond the Web
Gleich, D. F. (2015) · SIAM Review · doi:10.1137/140976649 · Cited by 656
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629
Comprehensible classification models
Freitas, A. A. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2594473.2594475 · Cited by 575
Learning the Structure of Dynamic Probabilistic NetworksPreprint
Friedman, N., Murphy, K., & Russell, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7374 · Cited by 560
Propagating Uncertainty in Bayesian Networks by Probabilistic Logic Sampling
Henrion, M. (1988) · Machine intelligence and pattern recognition · doi:10.1016/b978-0-444-70396-5.50019-4 · Cited by 551
Learning Bayesian Networks
Ramoni, M. F., & Sebastiani, P. (2005) · IGI Global eBooks · doi:10.4018/978-1-59140-557-3.ch128 · Cited by 542
A differential approach to inference in Bayesian networks
Darwiche, A. Y. (2003) · Journal of the ACM · doi:10.1145/765568.765570 · Cited by 498
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation
Aliferis, C., Statnikov, A., Tsamardinos, I., Mani, S., & Koutsoukos, X. (2010) · doi:10.5555/1756006.1756013 · Cited by 483
MIMIC: Finding Optima by Estimating Probability Densities
Bonet, J. S. D., Isbell, C. L., & Viola, P. A. (1996) · Cited by 471
An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., & Baesens, B. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.12.003 · Cited by 425
Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters
Larrañaga, P., Poza, M., Yurramendi, Y., Murga, R. H., & Kuijpers, C. M. H. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.537345 · Cited by 393
Toward Normative Expert Systems: Part I The Pathfinder Project
Heckerman, D. E., Horvitz, E., & Nathwani, B. N. (1992) · Methods of Information in Medicine · doi:10.1055/s-0038-1634867 · Cited by 365
Learning limited dependence Bayesian classifiers
Sahami, M. (1996) · Cited by 363
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000375277 · Cited by 360
The NIH BRAIN Initiative
Insel, T. R., Landis, S. C., & Collins, F. S. (2013) · Science · doi:10.1126/science.1239276 · Cited by 345
Finding MAPs for belief networks is NP-hard
Shimony, S. E. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90072-8 · Cited by 337
An analysis of physician attitudes regarding computer-based clinical consultation systems
Teach, R. L., & Shortliffe, E. H. (1981) · Computers and Biomedical Research · doi:10.1016/0010-4809(81)90012-4 · Cited by 328
The Human Brain Project
Markram, H. (2012) · Scientific American · doi:10.1038/scientificamerican0612-50 · Cited by 325
Simulation Approaches to General Probabilistic Inference on Belief Networks
Shachter, R. D., & Peot, M. A. (1990) · Machine intelligence and pattern recognition · doi:10.1016/b978-0-444-88738-2.50024-5 · Cited by 311
GABA progenitors grafted into the adult epileptic brain control seizures and abnormal behavior
Hunt, R. F., Girskis, K. M., Rubenstein, J. L. R., Álvarez-Buylla, A., & Baraban, S. C. (2013) · Nature Neuroscience · doi:10.1038/nn.3392 · Cited by 309
Chandelier cells and epilepsy
DeFelipe, J. (1999) · Brain · doi:10.1093/brain/122.10.1807 · Cited by 297
Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms.
Shwe, M., Blackford Middleton, A., Heckerman, D. E., Henrion, M., Horvitz, E., Lehmann, H. P., & Cooper, G. F. (1991) · PubMed · Cited by 293
The Human Brain Project: Creating a European Research Infrastructure to Decode the Human Brain
Amunts, K., Ebell, C. J., Müller, J. E., Telefont, M., Knoll, A., & Lippert, T. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.046 · Cited by 288
Learning Bayesian Network Model Structure from Data
Margaritis, D. (2003) · Cited by 282
Handbook of Combinatorial Optimization
Pardalos, P. M., 堵丁柱, & Graham, R. L. (2013) · Springer eBooks · doi:10.1007/978-1-4419-7997-1 · Cited by 276
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
Fung, R. M., & Chang, K. (1990) · Machine intelligence and pattern recognition · doi:10.1016/b978-0-444-88738-2.50023-3 · Cited by 268
Bayesian Networks and Probabilistic Inference in Forensic Science
Taroni, F., Aitken, C. G. G., Garbolino, P., & Biedermann, A. (2006) · Wiley eBooks · doi:10.1002/0470091754 · Cited by 265
Explainable AI for Designers: A Human-Centered Perspective on Mixed-Initiative Co-Creation
Zhu, J., Liapis, A., Risi, S., Bidarra, R., & Youngblood, G. M. (2018) · 2018 IEEE Conference on Computational Intelligence and Games (CIG) · doi:10.1109/cig.2018.8490433 · Cited by 255
Discrete Bayesian Network Classifiers: A Survey
Bielza, C., & Larrañaga, P. (2014) · ACM Computing Surveys · doi:10.1145/2576868 · Cited by 248
Self-citation
A review of explanation methods for Bayesian networks
Lacave, C., & Díez, F. J. (2002) · The Knowledge Engineering Review · doi:10.1017/s026988890200019x · Cited by 240
NESTOR: A Computer-Based Medical Diagnostic Aid That Integrates Causal and Probabilistic Knowledge.
Cooper, G. F. (1984) · Defense Technical Information Center (DTIC) · Cited by 221
Chapter 17 Cortical interneurons: from Cajal to 2001
DeFelipe, J. (2002) · Progress in brain research · doi:10.1016/s0079-6123(02)36019-9 · Cited by 194
Multi-dimensional classification with Bayesian networks
Bielza, C., Li, G., & Larrañaga, P. (2011) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2011.01.007 · Cited by 192
Handbook of Graphical Models
alberto, R., & luca, L. R. (2018) · CRC Press eBooks · doi:10.1201/9780429463976 · Cited by 187
The Logic of Explanatory Power
Schupbach, J. N., & Sprenger, J. (2011) · Philosophy of Science · doi:10.1086/658111 · Cited by 186
Multimeme Algorithms for Protein Structure Prediction
Krasnogor, N., Blackburne, B. P., Burke, E. K., & Hirst, J. D. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45712-7_74 · Cited by 182
Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base
Shwe, M., Heckerman, D. E., Henrion, M., Horvitz, E., Lehmann, H. P., Cooper, G. F., & Blackford Middleton, A. (1991) · Methods of Information in Medicine · doi:10.1055/s-0038-1634846 · Cited by 173
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions
Aliferis, C., Statnikov, A., Tsamardinos, I., Mani, S., & Koutsoukos, X. (2010) · doi:10.5555/1756006.1756014 · Cited by 163
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Larrañaga, P., Karshenas, H., Bielza, C., & Santana, R. (2013) · Information Sciences · doi:10.1016/j.ins.2012.12.051 · Cited by 162
Computer Aided Electromyography and Expert Systems
Desmedt, J. E. (1989) · Elsevier eBooks · Cited by 154
Bayesian networks in neuroscience: a survey
Bielza, C., & Larrañaga, P. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00131 · Cited by 152
Inhibitory Interneurons and their Circuit Motifs in the Many Layers of the Barrel Cortex
Feldmeyer, D., Qi, G., Emmenegger, V., & Staiger, J. F. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.05.027 · Cited by 140
Cortical basket cell dysfunction in schizophrenia
Curley, A. A., & Lewis, D. A. (2012) · The Journal of Physiology · doi:10.1113/jphysiol.2011.224659 · Cited by 136
Worldwide initiatives to advance brain research
Grillner, S. E., Ip, N. Y., Koch, C., Koroshetz, W. J., Okano, H., Polachek, M., Poo, M., & B, T. J. S. A. (2016) · Nature Neuroscience · doi:10.1038/nn.4371 · Cited by 136
Proceedings of the 10th International Conference on Neural Information Processing Systems
Jordan, M. I., Kearns, M., & Solla, S. A. (1996) · Cited by 126
Stakeholders in Explainable AI
Preece, A., Harborne, D., Braines, D., Tomsett, R., & Chakraborty, S. (2018) · arXiv · doi:10.48550/arxiv.1810.00184 · Cited by 117
Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables
Karshenas, H., Santana, R., Bielza, C., & Larrañaga, P. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2281524 · Cited by 109
Towards the automatic classification of neurons
Armañanzas, R., & Ascoli, G. A. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2015.02.004 · Cited by 106
Losing your inhibition: Linking cortical GABAergic interneurons to schizophrenia
Inan, M., Petros, T. J., & Anderson, S. A. (2012) · Neurobiology of Disease · doi:10.1016/j.nbd.2012.11.013 · Cited by 105
Proteome Analyst: custom predictions with explanations in a web-based tool for high-throughput proteome annotations
Szafron, D., Lu, P., Greiner, R., Wishart, D. S., Poulin, B., Eisner, R., Lu, Z., Anvik, J., Macdonell, C., Fyshe, A., et al. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh485 · Cited by 100
Constructive Induction of Cartesian Product Attributes
Pazzani, M. J. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5725-8_21 · Cited by 96
A review on probabilistic graphical models in evolutionary computation
Larrañaga, P., Karshenas, H., Bielza, C., & Santana, R. (2012) · Journal of Heuristics · doi:10.1007/s10732-012-9208-4 · Cited by 96
Targeted enrichment by solution-based hybrid capture to identify genetic sequence variants in barley
Hill, C. B., Wong, D., Tibbits, J. F. G., Forrest, K., Hayden, M., Zhang, X., Westcott, S., Angessa, T. T., & Li, C. (2019) · Scientific Data · doi:10.1038/s41597-019-0011-z · Cited by 96
Brain and other nervous system disorders across the lifespan — global challenges and opportunities
Silberberg, D. H., Anand, N. P., Michels, K. M., & Kalaria, R. N. (2015) · Nature · doi:10.1038/nature16028 · Cited by 94
Discovering Interesting Patterns for Investment Decision Making with GLOWER ☹—A Genetic Learner Overlaid with Entropy Reduction
Dhar, V., Chou, D., & Provost, F. J. (2000) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009848126475 · Cited by 85
LEARNING THE STRUCTURE OF AUGMENTED BAYESIAN CLASSIFIERS
Keogh, E., & Pazzani, M. J. (2002) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213002001052 · Cited by 83
Industrial Applications of Machine Learning
Larrañaga, P., Atienza, D., Díaz-Rozo, J., Ogbechie, A., Puerto-Santana, C., & Bielza, C. (2018) · CRC Press eBooks · doi:10.1201/9781351128384 · Cited by 80
Linkage information processing in distribution estimation algorithms
Bosman, P. A. N., & Thierens, D. (1999) · Utrecht University Repository (Utrecht University) · Cited by 78
The chandelier neuron in schizophrenia
Lewis, D. A. (2010) · Developmental Neurobiology · doi:10.1002/dneu.20825 · Cited by 76
A Comparative Computer Simulation of Dendritic Morphology
Donohue, D. E., & Ascoli, G. A. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000089 · Cited by 68
Elevated ErbB4 mRNA is related to interneuron deficit in prefrontal cortex in schizophrenia
Joshi, D., Fullerton, J. M., & Weickert, C. S. (2014) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2014.02.014 · Cited by 67
The Cerebral Neocortex
Eccles, J. C. (1984) · Cerebral cortex · doi:10.1007/978-1-4615-6610-6_1 · Cited by 66
Graphical Explanation in Belief Networks
Madigan, D., Mosurski, K., & Almond, R. G. (1997) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1997.10474735 · Cited by 62
Most Relevant Explanation in Bayesian NetworksPreprint
Yuan, C., Lim, H., & Lu, T.-C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1401.3893 · Cited by 61
It takes the world to understand the brain
Huang, Z. J., & Luo, L. (2015) · Science · doi:10.1126/science.aad4120 · Cited by 59
A Second Look at the Logic of Explanatory Power (with Two Novel Representation Theorems)
Crupi, V., & Tentori, K. (2012) · Philosophy of Science · doi:10.1086/666063 · Cited by 54
Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39)
Borchani, H., Bielza, C., Martı́nez-Martı́n, P., & Larrañaga, P. (2012) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2012.07.010 · Cited by 46
MUNIN:an expert EMG assistant
Andreassen, S., Jensen, F. V., Andersen, S. K., Falck, B., Kjærulff, U. B., Woldbye, M., Sørensen, A. R., Rosenfalck, A., & Jensen, F. (1989) · VBN Forskningsportal (Aalborg Universitet) · Cited by 46
Explanation, irrelevance and statistical independence
Shimony, S. E. (1991) · National Conference on Artificial Intelligence · Cited by 44
Multi-dimensional Bayesian network classifiers: A survey
Gil-Begue, S., Bielza, C., & Larrañaga, P. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09858-x · Cited by 40
General Organization of the Cerebral Cortex
White, E. L. (1989) · Birkhäuser Boston eBooks · doi:10.1007/978-1-4684-8721-3_1 · Cited by 38
Enhancement of Clinical Predictive Ability by Computer Consultation)
Dannenberg, A. L., Shapiro, A. R., & Fries, J. F. (1979) · Methods of Information in Medicine · doi:10.1055/s-0038-1636446 · Cited by 32
A community-based transcriptomics classification and nomenclature of neocortical cell types
Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Armañanzas, R., Ascoli, G. A., Bielza, C., Bokharaie, V., Bergmann, T. B., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0685-8 · Cited by 32
Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms
Armañanzas, R., Saeys, Y., Inza, I., García-Torres, M., Bielza, C., de Peer, Y. V., & Larrañaga, P. (2010) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2010.18 · Cited by 31
Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons
Mihaljević, B., Benavides‐Piccione, R., Bielza, C., DeFelipe, J., & Larrañaga, P. (2014) · Neuroinformatics · doi:10.1007/s12021-014-9254-1 · Cited by 29
Probability and the Explanatory Virtues
Glymour, C. (2014) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axt051 · Cited by 27
Bayesian network modeling of the consensus between experts: An application to neuron classification
López-Cruz, P. L., Larrañaga, P., DeFelipe, J., & Bielza, C. (2013) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2013.03.011 · Cited by 26
Classification of GABAergic interneurons by leading neuroscientists
Mihaljević, B., Benavides‐Piccione, R., Bielza, C., Larrañaga, P., & DeFelipe, J. (2019) · Scientific Data · doi:10.1038/s41597-019-0246-8 · Cited by 25
Handbook of Graphical Models
Allen, G. I. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1801279 · Cited by 24
Decision boundary for discrete Bayesian network classifiers
Varando, G., Bielza, C., & Larrañaga, P. (2015) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 23
bnclassify: Learning Bayesian Network Classifiers
Mihaljević, B., Bielza, C., & Larrañaga, P. (2019) · The R Journal · doi:10.32614/rj-2018-073 · Cited by 23
Floating search algorithm for structure learning of Bayesian network classifiers
Pernkopf, F., & O’Leary, P. (2003) · Pattern Recognition Letters · doi:10.1016/s0167-8655(03)00142-9 · Cited by 22
Towards a supervised classification of neocortical interneuron morphologies
Mihaljević, B., Larrañaga, P., Benavides‐Piccione, R., Hill, S., DeFelipe, J., & Bielza, C. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2470-1 · Cited by 20
Bearing fault diagnosis method based on the generalized S transform time–frequency spectrum de-noised by singular value decomposition
Cai, J., & Xiao, Y. (2018) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/0954406218782285 · Cited by 18
Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty
Mihaljević, B., Bielza, C., Benavides‐Piccione, R., DeFelipe, J., & Larrañaga, P. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00150 · Cited by 17
Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks
Mihaljević, B., Larrañaga, P., & Bielza, C. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.580873 · Cited by 17
SIMPLIFYING EXPLANATIONS IN BAYESIAN BELIEF NETWORKS
de Campos, L. M., Gámez, J. A., & Moral, S. (2001) · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488501000892 · Cited by 16
Classifying GABAergic interneurons with semi-supervised projected model-based clustering
Mihaljević, B., Benavides‐Piccione, R., Guerra, L., DeFelipe, J., Larrañaga, P., & Bielza, C. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.12.010 · Cited by 15
Network measures for information extraction in evolutionary algorithms
Santana, R., Armañanzas, R., Bielza, C., & Larrañaga, P. (2013) · International Journal of Computational Intelligence Systems · doi:10.1080/18756891.2013.823004 · Cited by 15
Comparing basal dendrite branches in human and mouse hippocampal CA1 pyramidal neurons with Bayesian networks
Mihaljević, B., Larrañaga, P., Benavides‐Piccione, R., DeFelipe, J., & Bielza, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-73617-9 · Cited by 14
Abductive Inference in Bayesian Networks: Finding a Partition of the Explanation Space
Flores, M. J., Gámez, J. A., & Moral, S. (2005) · Lecture notes in computer science · doi:10.1007/11518655_7 · Cited by 13
Coherence, Explanation, and Hypothesis Selection
Glass, D. H. (2018) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axy063 · Cited by 11
Data-Driven Computational Neuroscience
Bielza, C., & Larrañaga, P. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108642989 · Cited by 10
Most frugal explanations in Bayesian networks
Kwisthout, J. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2014.10.001 · Cited by 9
A Directional-Linear Bayesian Network and Its Application for Clustering and Simulation of Neural Somas
Luengo-Sánchez, S., Larrañaga, P., & Bielza, C. (2019) · IEEE Access · doi:10.1109/access.2019.2918494 · Cited by 8

Research works printed but not linked: 7

W. Samek, T. Wiegand, K.-R. Müller, Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models, arXiv preprint arXiv:1708.08296 (2017). · arXiv
Clancey, 1984, Use of MYCIN’s rules for tutoring, Rule-Based Expert Systems. Addison-Wesley, Reading, 20
Heckerman, 2000, Dependency networks for inference, collaborative filtering, and data visualization, J. Mach. Learn. Res., 1, 49
M. Henrion, M.J. Druzdzel, Uncertainty in Artificial Intelligence, Elsevier, 1991, pp. 17–32.
Good, 1985, Weight of evidence: A brief survey, Bayesian Stat., 2, 249
H.J. Suermondt, Explanation in Bayesian Belief Networks, Ph.D. thesis, Stanford, 1993.
R. Etxeberria, P. Larrañaga, Global optimization using Bayesian networks, in: Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99), pp. 151–173.

Sorted by citations

Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches
Borys, K., Schmitt, Y. A., Nauta, M., Seifert, C., Krämer, N., Friedrich, C. M., & Nensa, F. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110786 · Cited by 233
A scoping review of neurodegenerative manifestations in explainable digital phenotyping
Alfalahi, H., Dias, S. B., Khandoker, A. H., Chaudhuri, К. R., & Hadjileontiadis, L. J. (2023) · npj Parkinson s Disease · doi:10.1038/s41531-023-00494-0 · Cited by 58
Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives
Flammini, F., Alcaraz, C., Bellini, E., Marrone, S., López, J., & Bondavalli, A. (2022) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2022.3227113 · Cited by 43
Neuro-Symbolic Explainable Artificial Intelligence Twin for Zero-Touch IoE in Wireless Network
Munir, M. S., Kim, K. T., Adhikary, A., Saad, W., Shetty, S. S., Park, S.-B., & Hong, C. S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3303713 · Cited by 47
A systematic review of explainable artificial intelligence in urban building energy modeling: methods, applications, and future directions
Darvishvand, L., Kamkari, B., Huang, M. J., & Hewitt, N. J. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106492 · Cited by 42

See all 123

Authors: Mihaljević, B., Bielza, C., & Larrañaga, P.
Affiliations: Universidad Autónoma de Madrid (ES)
Universidad Politécnica de Madrid (ES)
Corresponding author: Mihaljević, Bojan
ORCID: Mihaljević, Bojan
Bielza, Concha
Larrañaga, Pedro
Venue: Neurocomputing
Publisher: Elsevier BV
Published: 2021-10
Volume: 456
Pages: 648-665
ISSN: 0925-2312 (ISSN-L)
DOI: 10.1016/j.neucom.2021.01.138
OpenAlex: W3173192496
Cited by: 123
References: 171
Citations by year: 2026: 19 · 2025: 34 · 2024: 31 · 2023: 22 · 2022: 16 · 2021: 1
FWCI: 5.90
Type: journal article
Language: en
Open access: green
oa.upm.es
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Neurocomputing (publishedVersion)
Hispana (submittedVersion, cc-by-nc-nd)
UPM Digital Archive (Technical University of Madrid) (submittedVersion, cc-by-nc-nd)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Keywords: Bayesian networks, model interpretability, probabilistic reasoning, joint probability distribution, conditional independence, directed acyclic graphs, estimation of distribution algorithms, heuristic optimization, evolutionary algorithms, anomaly detection, clustering, supervised classification, temporal modeling, bioinformatics applications, neuroscience applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Bayesian networks for interpretable machine learning and optimization

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks