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Extracting refined rules from knowledge-based neural networks
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Colowick, S. P., Joshi, M. D., Jagannathan, & KAPLAN, N. O. (1966) · Cited by 26,137
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1956) · Psychological Review · doi:10.1037/h0043158 · Cited by 17,812
The magical number seven, plus or minus two: Some limits on our capacity for processing information.
Miller, G. A. (1994) · Psychological Review · doi:10.1037/0033-295x.101.2.343 · Cited by 17,161
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,172
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Rumelhart, D. E., McClelland, J. L., & AU (1986) · doi:10.7551/mitpress/5236.001.0001 · Cited by 15,686
(1975) · CERN Document Server (European Organization for Nuclear Research) · Cited by 5,958
(1989) · Cited by 3,214
Gordon, A. D., & Hartigan, J. A. (1976) · Journal of the American Statistical Association · doi:10.2307/2286880 · Cited by 2,569
Quinlan, J. R. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80053-6 · Cited by 2,553
The Cascade-Correlation learning architecture
Fahlman, S. E., & Lebiere, C. J. (2018) · doi:10.1184/r1/6610403 · Cited by 2,372
Cited by
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.10045 · Cited by 11,321
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
Explaining Explanations: An Overview of Interpretability of Machine Learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2018.00018 · Cited by 2,507
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E., & Kononenko, I. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-013-0679-x · Cited by 2,286
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
Survey and critique of techniques for extracting rules from trained artificial neural networks
Andrews, R., Diederich, J., & Tickle, A. B. (1995) · Knowledge-Based Systems · doi:10.1016/0950-7051(96)81920-4 · Cited by 1,225
A Survey on Neural Network Interpretability
Zhang, Y., Tino, P., Leonardis, A., & Tang, K. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3100641 · Cited by 923
Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
Saeed, W., & Omlin, C. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110273 · Cited by 783
Knowledge-based artificial neural networks
Towell, G. G., & Shavlik, J. W. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90105-8 · Cited by 746
Self-citationFrom implicit skills to explicit knowledge: a bottom-up model of skill learning
SUN, R., MERRILL, E., & PETERSON, T. (2001) · Cognitive Science · doi:10.1016/s0364-0213(01)00035-0 · Cited by 474
PRADA: Protecting Against DNN Model Stealing Attacks
Juuti, M., Szyller, S., Marchal, S., & Asokan, N. (2019) · 2019 IEEE European Symposium on Security and Privacy (EuroS&P) · doi:10.1109/eurosp.2019.00044 · Cited by 440
Tickle, A. B., Andrews, R., Golea, M., & Diederich, J. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.728352 · Cited by 438
Supersparse linear integer models for optimized medical scoring systems
Ustun, B., & Rudin, C. (2016) · Machine Learning · doi:10.1007/s10994-015-5528-6 · Cited by 313
Using Sampling and Queries to Extract Rules from Trained Neural Networks
Craven, M. W., & Shavlik, J. W. (1994) · Machine Learning Proceedings 1994 · doi:10.1016/b978-1-55860-335-6.50013-1 · Cited by 311
Self-citationKnowledge graphs as tools for explainable machine learning: A survey
Tiddi, I., & Schlobach, S. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2021.103627 · Cited by 283
Causal interpretability for machine learning-problems, methods and evaluation
Moraffah, R., Karami, M., Guo, R., Raglin, A., & Liu, H. (2020) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3400051.3400058 · Cited by 238
Interpretation of Quantitative Structure-Activity Relationship Models: Past, Present, and Future
Polishchuk, P. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00274 · Cited by 236
A review of some techniques for inclusion of domain-knowledge into deep neural networks
Dash, T., Chitlangia, S., Ahuja, A., & Srinivasan, A. (2022) · Scientific Reports · doi:10.1038/s41598-021-04590-0 · Cited by 196
Explainable Artificial Intelligence for Tabular Data: A Survey
Sahakyan, M., Aung, Z., & Rahwan, T. (2021) · IEEE Access · doi:10.1109/access.2021.3116481 · Cited by 135
Survey on Explainable AI: Techniques, challenges and open issues
Abusitta, A., Li, M. Q., & Fung, B. C. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124710 · Cited by 98
Learning, action and consciousness: a hybrid approach toward modelling consciousness
Sun, R. (1997) · Neural Networks · doi:10.1016/s0893-6080(97)00050-6 · Cited by 71
(2020) · doi:10.1007/978-3-030-51924-7_4 · Cited by 31
Risk and Exposure of XAI in Persuasion and Argumentation: The case of Manipulation
Carli, R., Najjar, A., & Calvaresi, D. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-15565-9_13 · Cited by 18
Metadata
- Authors
- Towell, G. G., & Shavlik, J. W.
- Venue
- Machine Learning
- Published
- 1993-10
- Volume
- 13
- Issue
- 1
- Pages
- 71-101
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1989574962
- Cited by
- 757
- References
- 38
- FWCI
- 26.66
- Type
- journal article
- Language
- en
- Open access
- bronze
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