Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

Fuzzy sets

Zadeh, L. A. (1965) · Information and Control · doi:10.1016/s0019-9958(65)90241-x · Cited by 67,855

Classification and Regression Trees.

Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867

Unpaired image-to-image translation using cycle-consistent adversarial networks

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602

Untitled work

(1992) · Cited by 23,431

Image-to-Image Translation with Conditional Adversarial Networks

Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930

Induction of decision trees

Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119

Mining association rules between sets of items in large databases

Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696

Unsupervised representation learning with deep convolutional generative adversarial networks

Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235

Learning with Kernels

Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934

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

Extremely randomized trees

Geurts, P., Ernst, D., & Wehenkel, L. (2006) · Machine Learning · doi:10.1007/s10994-006-6226-1 · Cited by 9,300

An Experimental Application of the DELPHI Method to the Use of Experts

Dalkey, N., & Helmer, O. (1963) · Management Science · doi:10.1287/mnsc.9.3.458 · Cited by 6,061

Context Encoders: Feature Learning by Inpainting

Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691

An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression

Altman, N. (1992) · The American Statistician · doi:10.1080/00031305.1992.10475879 · Cited by 5,657

Instance-Based Learning Algorithms

Aha, D. W., Kibler, D. F., & Albert, M. K. (1991) · Machine Learning · doi:10.1023/a:1022689900470 · Cited by 5,290

Progressive Growing of GANs for Improved Quality, Stability, and Variation

Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017) · arXiv · doi:10.48550/arxiv.1710.10196 · Cited by 4,727

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Tao, A., Kautz, J., & Catanzaro, B. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00917 · Cited by 4,622

Mining association rules between sets of items in large databases

Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551

The Delphi Technique: Making Sense of Consensus

Hsu, C.-C., & Sandford, B. A. (2007) · University of Massachusetts Amherst · doi:10.7275/pdz9-th90 · Cited by 4,500 · cited 4×

Untitled work

(1989) · neural information processing systems · Cited by 3,736

Large Scale GAN Training for High Fidelity Natural Image Synthesis

Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717

Artificial neural networks: fundamentals, computing, design, and application

Basheer, I. A., & Hajmeer, M. N. (2000) · Journal of Microbiological Methods · doi:10.1016/s0167-7012(00)00201-3 · Cited by 3,316

StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks

Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.629 · Cited by 3,021

Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning

Fayyad, U. M., & Irani, K. B. (1993) · International Joint Conference on Artificial Intelligence · Cited by 2,870

Neural Discrete Representation Learning

Oord, A. V. D., Vinyals, O., & Kavukcuoglu, K. (2017) · arXiv · doi:10.48550/arxiv.1711.00937 · Cited by 2,729

Markov logic networks

Richardson, M., & Domingos, P. (2006) · Machine Learning · doi:10.1007/s10994-006-5833-1 · Cited by 2,676

Generative Adversarial Text to Image Synthesis

Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv · doi:10.48550/arxiv.1605.05396 · Cited by 2,494

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial NetworksPreprint

Zhu, J.-Y., Park, T., Isola, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10593 · Cited by 2,278

Principles of QSAR models validation: internal and external

Gramatica, P. (2007) · QSAR & Combinatorial Science · doi:10.1002/qsar.200610151 · Cited by 2,249

Introduction to artificial neural systems

Nelson, D. E., & Wang, J. (1992) · Neurocomputing · doi:10.1016/0925-2312(92)90018-k · Cited by 2,011

Generative adversarial network in medical imaging: A review

Xin, Y., Walia, E., & Babyn, P. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101552 · Cited by 1,959

Decision trees: a recent overview

Kotsiantis, S. B. (2011) · Artificial Intelligence Review · doi:10.1007/s10462-011-9272-4 · Cited by 1,660

Fuzzy support vector machines

Lin, C.-F., & Wang, S. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/72.991432 · Cited by 1,496

Coupled Generative Adversarial NetworksPreprint

Liu, M.-Y., & Tuzel, O. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.07536 · Cited by 1,445

An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression

Altman, N. (1992) · The American Statistician · doi:10.2307/2685209 · Cited by 1,420

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Wu, J., Zhang, C., Xue, T., Freeman, W. T., & Tenenbaum, J. B. (2016) · arXiv · doi:10.48550/arxiv.1610.07584 · Cited by 1,393

Semantic Image Inpainting with Deep Generative Models

Yeh, R. A., Chen, C., Lim, T. Y., Schwing, A. G., Hasegawa‐Johnson, M., & Do, M. N. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.728 · Cited by 1,348

Efficiently mining long patterns from databases

Bayardo, R. J. (1998) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/276304.276313 · Cited by 1,324

Image De-Raining Using a Conditional Generative Adversarial Network

Zhang, H., Sindagi, V. A., & Patel, V. M. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2920407 · Cited by 1,268

A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data

Jansen, R., Yu, H., Greenbaum, D. S., Kluger, Y., Krogan, N. J., Chung, S., Emili, A., Snyder, M. P., Greenblatt, J. F., & Gerstein, M. (2003) · Science · doi:10.1126/science.1087361 · Cited by 1,254

A Tutorial on Learning with Bayesian Networks

Heckerman, D. E. (1998) · Springer eBooks · doi:10.1007/978-94-011-5014-9_11 · Cited by 1,242

Age Progression/Regression by Conditional Adversarial Autoencoder

Zhang, Z., Song, Y., & Qi, H. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.463 · Cited by 1,216

Interestingness measures for data mining

Geng, L., & Hamilton, H. J. (2006) · ACM Computing Surveys · doi:10.1145/1132960.1132963 · Cited by 1,183

Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Samek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080

Efficient mining of emerging patterns

Dong, G., & Li, J. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312191 · Cited by 1,030

Generating Videos with Scene DynamicsPreprint

Vondrick, C., Pirsiavash, H., & Torralba, A. B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.02612 · Cited by 976

Unsupervised Cross-Domain Image GenerationPreprint

Taigman, Y., Polyak, A., & Wolf, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.02200 · Cited by 956

Is Combining Classifiers with Stacking Better than Selecting the Best One?

Džeroski, S., & Ženko, B. (2004) · Machine Learning · doi:10.1023/b:mach.0000015881.36452.6e · Cited by 890

Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition

Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q. (2015) · PLOS ONE · doi:10.1371/journal.pone.0140381 · Cited by 879 · cited 1×

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1802.07228 · Cited by 849

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · Apollo - University of Cambridge Repository · doi:10.17863/cam.22520 · Cited by 849

Understanding deep convolutional networks

Mallat, S. (2016) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2015.0203 · Cited by 733

Pose Guided Person Image GenerationPreprint

Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., & Gool, L. V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.09368 · Cited by 719

Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis

Huang, R., Zhang, S., Li, T., & He, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.267 · Cited by 705

Detecting and classifying lesions in mammograms with Deep Learning

Ribli, D., Horváth, A., Unger, Z., Pollner, P., & Csabai, I. (2018) · Scientific Reports · doi:10.1038/s41598-018-22437-z · Cited by 696

Generative Face Completion

Li, Y., Liu, S., Yang, J., & Yang, M. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.624 · Cited by 668

Face aging with conditional generative adversarial networks

Antipov, G., Baccouche, M., & Dugelay, J. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296650 · Cited by 597

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

Deep Neural Decision Forests

Kontschieder, P., Fiterau, M., Criminisi, A., & Bulò, S. R. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.172 · Cited by 535

Seizure characteristics and control following resection in 332 patients with low-grade gliomas

Chang, E. F., Potts, M. B., Keles, G. E., Lamborn, K. R., Chang, S. M., Barbaro, N. M., & Berger, M. S. (2008) · Journal of neurosurgery · doi:10.3171/jns/2008/108/2/0227 · Cited by 528

Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links

Shen, H., Zhu, Y., Zhang, L., & Park, J. H. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2511196 · Cited by 489

Comprehensible credit scoring models using rule extraction from support vector machines

Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.04.051 · Cited by 448

Learning What and Where to DrawPreprint

Reed, S., Akata, Z., Mohan, S., Tenka, S., Schiele, B., & Lee, H. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.02454 · Cited by 444

Data Mining with Decision Trees

Rokach, L., & Maimon, O. (2013) · Series in machine perception and artificial intelligence · doi:10.1142/9097 · Cited by 406

Series in Machine Perception and Artificial Intelligence

(2012) · Series in machine perception and artificial intelligence · doi:10.1142/smpai · Cited by 373

Predicting the Future: Have you considered using the Delphi Methodology?

Ludwig, B. (1997) · Journal of Extension · doi:10.66752/1077-5315.8000 · Cited by 348

Monocular Depth Estimation Using Neural Regression Forest

Roy, A., & Todorović, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.594 · Cited by 319

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANsPreprint

Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.11585 · Cited by 301

Support Vector Machines Applications

Ma, Y., Guo, G., & Publishing, S. I. (2014) · Springer eBooks · doi:10.1007/978-3-319-02300-7 · Cited by 292

Application of Bayesian Networks in Reliability Evaluation

Cai, B., Kong, X., Liu, Y., Lin, J., Yuan, X., Xu, H., & Ji, R. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2858281 · Cited by 280

Neural Photo Editing with Introspective Adversarial Networks

Brock, A., Lim, T., Ritchie, J. M., & Weston, N. (2016) · arXiv · doi:10.48550/arxiv.1609.07093 · Cited by 275

3D Shape Induction from 2D Views of Multiple Objects

Gadelha, M., Maji, S., & Wang, R. (2017) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00053 · Cited by 261

DeepRED – Rule Extraction from Deep Neural Networks

Zilke, J. R., Mencía, E. L., & Janssen, F. W. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46307-0_29 · Cited by 250

Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning

Corani, G. (2005) · Ecological Modelling · doi:10.1016/j.ecolmodel.2005.01.008 · Cited by 246

Image De-raining Using a Conditional Generative Adversarial NetworkPreprint

Zhang, H., Sindagi, V. A., & Patel, V. M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.05957 · Cited by 245

A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis

Ray, P., & Chakrabarti, A. (2019) · Applied Computing and Informatics · doi:10.1016/j.aci.2019.02.002 · Cited by 224

Methodological and conceptual issues confronting a cross-country Delphi study of educational program evaluation

Hung, H. S., Altschuld, J. W., & Lee, Y. (2008) · Evaluation and Program Planning · doi:10.1016/j.evalprogplan.2008.02.005 · Cited by 205

Deep Neural Decision TreesPreprint

Yang, Y., Morillo, I. G., & Hospedales, T. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.06988 · Cited by 188

BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification

Haixiang, G., Li, Y., Li, Y., Xiao, L., & Jinling, L. (2015) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2015.09.011 · Cited by 185

Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases

Loyola‐González, O., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A., & García-Borroto, M. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.04.120 · Cited by 185

Towards the Automatic Anime Characters Creation with Generative Adversarial NetworksPreprint

Jin, Y., Zhang, J., Li, M., Tian, Y., Zhu, H., & Fang, Z. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.05509 · Cited by 180

Efficient search for association rules

Webb, G. I. (2000) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/347090.347112 · Cited by 165

Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets

Liu, W., & Chawla, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20847-8_29 · Cited by 157

Bayesian learning theory applied to human cognition

Jacobs, R. A., & Kruschke, J. K. (2010) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.80 · Cited by 149

TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial NetworkPreprint

Dash, A., Gamboa, J. C. B., Ahmed, S., Liwicki, M., & Afzal, M. Z. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.06412 · Cited by 142

Extended local binary patterns for face recognition

Liu, L., Fieguth, P., Zhao, G., Pietikäinen, M., & Hu, D. (2016) · Information Sciences · doi:10.1016/j.ins.2016.04.021 · Cited by 139

Please Stop Explaining Black Box Models for High Stakes Decisions.Preprint

Rudin, C. D. (2018) · arXiv (Cornell University) · Cited by 138

Neural Decision Forests for Semantic Image Labelling

Bulò, S. R., & Kontschieder, P. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.18 · Cited by 133

Using Bayesian network for fault location on distribution feeder

Chien, C., Chen, S., & Lin, Y.-S. (2002) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2002.1022804 · Cited by 132

A rule-based system for document image segmentation

Fisher, J. L., Hinds, S. C., & D'Amato, D. P. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1990.118166 · Cited by 121

A novel approach for software defect prediction through hybridizing gradual relational association rules with artificial neural networks

Miholca, D.-L., Czibula, G., & Czibula, I. G. (2018) · Information Sciences · doi:10.1016/j.ins.2018.02.027 · Cited by 119

Applying lazy learning algorithms to tackle concept drift in spam filtering

Fdez‐Riverola, F., Iglesias, E. L., Díaz, F., Méndez, J. R., & Corchado, J. M. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.04.011 · Cited by 118

GP-GAN: Towards Realistic High-Resolution Image BlendingPreprint

Wu, H., Zheng, S., Zhang, J., & Huang, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07195 · Cited by 111

Convolutional Neural Networks for Page Segmentation of Historical Document Images

Chen, K., Seuret, M., Hennebert, J., & Ingold, R. (2017) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2017.161 · Cited by 101

Age Progression/Regression by Conditional Adversarial AutoencoderPreprint

Zhang, Z., Song, Y., & Qi, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08423 · Cited by 98

Iris recognition based on score level fusion by using SVM

Park, H., & Park, K. R. (2007) · Pattern Recognition Letters · doi:10.1016/j.patrec.2007.05.017 · Cited by 97

Neural Random Forests

Gérard Biau, A., Scornet, E., & Welbl, J. (2018) · Sankhya A · doi:10.1007/s13171-018-0133-y · Cited by 95

Assessing web sites quality: A systematic literature review by text and association rules mining

Rekik, R., Kallel, I., Casillas, J., & Alimi, A. M. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.06.007 · Cited by 91

Generative Face CompletionPreprint

Li, Y., Liu, S., Yang, J., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.05838 · Cited by 91

Exploring shipping accident contributory factors using association rules

Weng, J., & Li, G. (2017) · Journal of Transportation Safety & Security · doi:10.1080/19439962.2017.1341440 · Cited by 88

Detection of Frequent Alarm Patterns in Industrial Alarm Floods Using Itemset Mining Methods

Hu, W., Chen, T., & Shah, S. L. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2795573 · Cited by 88

A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation

Valdés-Ramírez, D., Medina‐Pérez, M. A., Monroy, R., Loyola‐González, O., Rodríguez‐Ruiz, J., Morales, A., & Herrera, F. (2019) · IEEE Access · doi:10.1109/access.2019.2909497 · Cited by 87

A Bayesian network fault diagnostic system for proton exchange membrane fuel cells

Riascos, L. A. M., Simões, M. G., & Miyagi, P. E. (2007) · Journal of Power Sources · doi:10.1016/j.jpowsour.2006.12.003 · Cited by 86

Modeling of a magnetorheological damper by recursive lazy learning

Boada, M. J. L., Calvo, J. A., Boada, B. L., & Díaz, V. (2008) · International Journal of Non-Linear Mechanics · doi:10.1016/j.ijnonlinmec.2008.11.019 · Cited by 83

Contrast Pattern-Based Classification for Bot Detection on Twitter

Loyola‐González, O., Monroy, R., Rodríguez‐Ruiz, J., López‐Cuevas, A., & Mata-Sánchez, J. I. (2019) · IEEE Access · doi:10.1109/access.2019.2904220 · Cited by 80

Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees

Ly, H., Monteiro, É., Le, T.-T., Le, M. V., Dal, M., Régnier, G., & Pham, B. T. (2019) · Materials · doi:10.3390/ma12091544 · Cited by 80

Rule Extraction Algorithm for Deep Neural Networks: A ReviewPreprint

Hailesilassie, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.05267 · Cited by 76

Part-of-speech tagging using decision trees

Màrquez, L., & Rodríguez, H. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026668 · Cited by 74

Relationship Between Student Engagement and Performance in E-Learning Environment Using Association Rules

Moubayed, A., Injadat, M., Shami, A. A., & Lutfiyya, H. L. (2018) · IEEE World Engineering Education Conference (EDUNINE) · doi:10.1109/edunine.2018.8451005 · Cited by 72

Cost-Sensitive Decision Trees Applied to Medical Data

Freitas, A., Costa‐Pereira, A., & Brazdil, P. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74553-2_28 · Cited by 71

PBC4cip: A new contrast pattern-based classifier for class imbalance problems

Loyola‐González, O., Medina‐Pérez, M. A., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A., Monroy, R., & García-Borroto, M. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.10.018 · Cited by 71

Pervasive social networking forensics: Intelligence and evidence from mobile device extracts

Quick, D., & Choo, K. R. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.11.018 · Cited by 70

Fingerprint Classification with Combinations of Support Vector Machines

Yao, Y., Frasconi, P., & Pontil, M. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45344-x_37 · Cited by 68

Semantic Image Inpainting with Deep Generative ModelsPreprint

Yeh, R. A., Chen, C., Lim, T. Y., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.07539 · Cited by 68

Analyzing Perception-Distortion Tradeoff Using Enhanced Perceptual Super-Resolution Network

Vasu, S., Madam, N. T., & Rajagopalan, A. N. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11021-5_8 · Cited by 67

Induction of Decision Trees and Bayesian Classification Applied to Diagnosis of Sport Injuries

Zelič, I., Kononenko, I., Nada Lavrač, A., & Vuga, V. (1997) · Journal of Medical Systems · doi:10.1023/a:1022880431298 · Cited by 65

Edited AdaBoost by weighted kNN

Gao, Y., & Gao, F. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.06.024 · Cited by 64

Choosing the Right Lens: Finding What is Interesting in Data Mining

Geng, L., & Hamilton, H. J. (2007) · Studies in computational intelligence · doi:10.1007/978-3-540-44918-8_1 · Cited by 60

BAYESIAN NETWORKS IN EDUCATIONAL TESTING

Vomlel, J. (2004) · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · doi:10.1142/s021848850400259x · Cited by 60

IoT Device Forensics and Data Reduction

Quick, D., & Choo, K. R. (2018) · IEEE Access · doi:10.1109/access.2018.2867466 · Cited by 60

Combining emerging patterns with random forest for complex activity recognition in smart homes

Malazi, H. T., & Davari, M. (2017) · Applied Intelligence · doi:10.1007/s10489-017-0976-2 · Cited by 57

Enabling Fast Lazy Learning for Data Streams

Zhang, P., Gao, B. J., Xingquan Zhu, A., & Guo, L. (2011) · Proceedings · doi:10.1109/icdm.2011.63 · Cited by 56

A Neuro-Cognitive Theory of Deductive Relational Reasoning with Mental Models and Visual Images

Knauff, M. (2009) · Spatial Cognition and Computation · doi:10.1080/13875860902887605 · Cited by 52

Classification of Cancer Data: Analyzing Gene Expression Data Using a Fuzzy Decision Tree Algorithm

Ludwig, S. A., Picek, S., & Jakobović, D. (2017) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-319-65455-3_13 · Cited by 49

The underutilization of information and communication technology-assisted collaborative project-based learning among international educators: a Delphi study

Kramer, B. S., Walker, A. E., & Brill, J. M. (2007) · Educational Technology Research and Development · doi:10.1007/s11423-007-9048-3 · Cited by 48

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Cavadenti, O., Codocedo, V., Boulicaut, J., & Kaytoue, M. (2016) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2016.75 · Cited by 44

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Escalante, H. J., Villatoro-Tello, E., Garza, S. E., López-Monroy, A. P., Montes-y-Gómez, M., & Villaseñor-Pineda, L. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.07.040 · Cited by 44

Discriminative subprofile-specific representations for author profiling in social media

López-Monroy, A. P., Montes-y-Gómez, M., Escalante, H. J., Villaseñor-Pineda, L., & Stamatatos, E. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.06.024 · Cited by 42

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Lara-Estrada, L. D., Rasche, L., Sucar, L. E., & Schneider, U. A. (2018) · Land · doi:10.3390/land7010004 · Cited by 40

Data mining combined to the multicriteria decision analysis for the improvement of road safety: case of France

Mazouri, F. Z. E., Abounaima, M. C., & Zenkouar, K. (2019) · Journal Of Big Data · doi:10.1186/s40537-018-0165-0 · Cited by 40

Learning to Explain With Complemental Examples

Kanehira, A., & Harada, T. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00880 · Cited by 39

Ranking of text documents using TF-IDF weighting and association rules mining

Jabri, S., Dahbi, A., Gadi, T., & Bassir, A. (2018) · International Conference on Optimization and Applications (ICOA) · doi:10.1109/icoa.2018.8370597 · Cited by 37

Tool-Wear Monitoring Based on Continuous Hidden Markov Models

Vallejo, A. G., Nolazco‐Flores, J. A., Morales-Menéndez, R., Sucar, L. E., & Rodriguez, C. A. (2005) · Lecture notes in computer science · doi:10.1007/11578079_91 · Cited by 36

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Bao, Y., Ishii, N., & Du, X. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-28651-6_93 · Cited by 36

Logical reasoning, world knowledge, and mental imagery: Interconnections in cognitive processes

Clement, C. A., & Falmagne, R. J. (1986) · Memory & Cognition · doi:10.3758/bf03202507 · Cited by 36

Fuzzy emerging patterns for classifying hard domains

García-Borroto, M., Martínez-Trinidad, J. F., & Carrasco-Ochoa, J. A. (2010) · Knowledge and Information Systems · doi:10.1007/s10115-010-0324-x · Cited by 36

Bayesian network analysis of resistance pathways against HIV-1 protease inhibitors

Deforche, K., Camacho, R. J., Grossman, Z., Silander, T., Soares, M. A., Moreau, Y., Shafer, R. W., Laethem, K. V., Carvalho, A. P., & Wynhoven, B. (2006) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2006.09.004 · Cited by 33

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Ribeiro-Neto, B. A., Silva, I., & Muntz, R. R. (2000) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1849-9_11 · Cited by 32

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Chen, C., Li, X., Belkacem, A. N., Qiao, Z., Dong, E., Tan, W., & Shin, D. (2019) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-019-00102-3 · Cited by 31

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Shi, S., & Weigend, A. S. (2002) · doi:10.1109/cifer.1997.618944 · Cited by 30

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García-Borroto, M., Martínez-Trinidad, J. F., & Carrasco-Ochoa, J. A. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.02.028 · Cited by 29

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Carnevale, C., Finzi, G., Pederzoli, A., Turrini, E., & Volta, M. (2016) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2016.04.022 · Cited by 29

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Loyola‐González, O., López‐Cuevas, A., Medina‐Pérez, M. A., Camiña, J. B., Ramírez-Márquez, J. E., & Monroy, R. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.05.004 · Cited by 29

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Antal, P., Verrelst, H., Timmerman, D., Moreau, Y., Huffel, S. V., Moor, B. D., & Vergote, I. B. (2002) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2000.856886 · Cited by 24

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Yamada, T., Yamashita, K., Ishii, N., & Iwata, K. (2006) · ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) · doi:10.1109/snpd-sawn.2006.69 · Cited by 21

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Johnson, J. C., Luczkovich, J. J., Borgatti, S. P., & Snijders, T. A. B. (2009) · Ecological Modelling · doi:10.1016/j.ecolmodel.2009.06.037 · Cited by 21

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Martínez-Díaz, Y., Hernández, N., Biscay, R., Chang, L., Méndez-Vázquez, H., & Sucar, L. E. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.01.017 · Cited by 19

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Gadelha, M., Maji, S., & Wang, R. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.05872 · Cited by 19

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Ortega-Mendoza, R. M., López-Monroy, A. P., Franco-Árcega, A., & Montes-y-Gómez, M. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.01.014 · Cited by 18

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Wulandari, C. P., Ouyang, C., & Wang, H. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.044 · Cited by 18

Effect of class imbalance on quality measures for contrast patterns: An experimental study

Loyola‐González, O., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A., & García-Borroto, M. (2016) · Information Sciences · doi:10.1016/j.ins.2016.09.040 · Cited by 17

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Weng, C.-H., & Tony, C. K. H. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1231-1 · Cited by 17

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Martínez-Díaz, Y., Méndez-Vázquez, H., López-Avila, L., Chang, L., Sucar, L. E., & Massimo Tistarelli, A. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00082 · Cited by 17

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Dami, S., Barforoush, A. A., & Shirazi, H. M. (2016) · Journal of Information Science · doi:10.1177/0165551516673285 · Cited by 15

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Loyola‐González, O., Medina‐Pérez, M. A., Hernández-Tamayo, D., Monroy, R., Carrasco-Ochoa, J. A., & García-Borroto, M. (2019) · Sensors · doi:10.3390/s19020414 · Cited by 14

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Baek, J., & Kim, E. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.10.032 · Cited by 13

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Kück, M., & Scholz‐Reiter, B. (2013) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2013.183 · Cited by 12

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Topçu, B., & Erdoğan, H. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.11.029 · Cited by 12

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Bansal, A., Agarwal, R., & Sharma, R. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-8968-8_7 · Cited by 11

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Loyola‐González, O., Monroy, R., Medina‐Pérez, M. A., Cervantes, B., & Grimaldo-Tijerina, J. E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04491-6_21 · Cited by 11

Overview and Analysis of Contrast Pattern Based Classification

Zhang, X., & Dong, G. (2012) · Chapman & Hall/CRC data mining and knowledge discovery series · doi:10.1201/b12986-16 · Cited by 11

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SOLOMATINE, D. P., Maskey, M. L., & Shrestha, D. L. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.1716773 · Cited by 10

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Chavary, E. A., Erfani, S., & Leckie, C. A. (2017) · doi:10.1145/3132847.3133111 · Cited by 9

On the Use of Decision Trees Based on Diagnosis and Drug Codes for Analyzing Chronic Patients

Soguero-Ruíz, C., Díaz-Plaza, A. A., de Miguel-Bohoyo, P., Ramos, J., Rubio-Sánchez, M., Sánchez, A., & Mora-Jiménez, I. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78759-6_14 · Cited by 8

Lazy Learning for Predictive Toxicology based on a Chemical Ontology

Armengol, E., & Plaza, E. (2007) · Springer eBooks · doi:10.1007/978-1-4020-5811-0_1 · Cited by 8

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Pérez, N. C., Denis-Miranda, L. A., Hernández-Palacio, R., Espino, M. M., & García-Borroto, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01132-1_29 · Cited by 7

Preliminaries

Dong, G. (2012) · Chapman & Hall/CRC data mining and knowledge discovery series · doi:10.1201/b12986-3 · Cited by 7

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Lai, C.-P., & Lu, J. (2018) · Asia Pacific Management Review · doi:10.1016/j.apmrv.2018.01.005 · Cited by 6

Lazy Learning for Predictive Toxicology based on a Chemical Ontology

Armengol, E., & Plaza, E. (2006) · Springer eBooks · doi:10.1007/1-4020-2865-2_1 · Cited by 6

Exploiting the Power of Group Differences

Dong, G. (2019) · Synthesis lectures on data mining and knowledge discovery · doi:10.2200/s00897ed1v01y201901dmk016 · Cited by 5

Detecting Pneumatic Failures on Temporary Immersion Bioreactors

Loyola‐González, O., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A., Hernández-Tamayo, D., & García-Borroto, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39393-3_29 · Cited by 4

Hierarchical Markov Random Fields with Irregular Pyramids for Improving Image Annotation

Morales-González, A., García-Reyes, E. B., & Sucar, L. E. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34654-5_53 · Cited by 2

Inferring Causal Gene Regulatory Networks Using Time-Delay Association Rules

Ahmed, S. S., Roy, S., & Choudhury, P. P. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-3143-5_26 · Cited by 1

Association Rule Construction from Crime Pattern Through Novelty Approach

Usha, D., Rameshkumar, K., & Baiju, B. V. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-1882-5_22 · Cited by 1

Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution NetworkPreprint

Vasu, S., Madam, N. T., & Rajagopalan, A. N. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.00344

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

Human-in-the-loop machine learning: a state of the art

Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10246-w · Cited by 1,251

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Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022) · Information Systems Frontiers · doi:10.1007/s10796-021-10186-w · Cited by 1,046

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Khan, A. A., Chaudhari, O., & Chandra, R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122778 · Cited by 677

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Alkadi, O., Moustafa, N., Turnbull, B., & Choo, K. R. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2996590 · Cited by 489

Binary Classification for Enhancing Explainability of Combined Black-Box and White-Box Models

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