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Fast Effective Rule Induction
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Cites
Learning Logical Definitions from Relations
Quinlan, J. R. (1990) · Machine Learning · doi:10.1023/a:1022699322624 · Cited by 1,644
Cited by
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
Content-Based Recommendation Systems
Pazzani, M. J., & Billsus, D. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72079-9_10 · Cited by 2,600
Anomaly-based network intrusion detection: Techniques, systems and challenges
García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009) · Computers & Security · doi:10.1016/j.cose.2008.08.003 · Cited by 2,085
Machine learning: a review of classification and combining techniques
Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006) · Artificial Intelligence Review · doi:10.1007/s10462-007-9052-3 · Cited by 1,582
Sentiment strength detection in short informal text
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21416 · Cited by 1,449
Mining with rarity: a unifying framework
Weiss, G. M. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007734 · Cited by 1,393
Predictive learning via rule ensembles
Friedman, J. H., & Popescu, B. E. (2008) · The Annals of Applied Statistics · doi:10.1214/07-aoas148 · Cited by 1,315
A Survey on the Explainability of Supervised Machine Learning
Burkart, N., & Huber, M. F. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12228 · Cited by 1,008
Interpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958
CPAR: Classification based on Predictive Association Rules
Yin, X., & Han, J. (2003) · Proceedings of the 2003 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972733.40 · Cited by 833
Clustering-based undersampling in class-imbalanced data
Lin, W.-C., Tsai, C.-F., Hu, Y.-H., & Jhang, J.-S. (2017) · Information Sciences · doi:10.1016/j.ins.2017.05.008 · Cited by 832
On the Class Imbalance Problem
Guo, X., Yin, Y., Dong, C., Yang, G., & Zhou, G. (2008) · 2008 Fourth International Conference on Natural Computation · doi:10.1109/icnc.2008.871 · Cited by 495
Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.09.031 · Cited by 462
The Role of Occam's Razor in Knowledge Discovery
Domingos, P. (1999) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009868929893 · Cited by 459
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
A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596
Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
Ghotra, B., McIntosh, S., & Hassan, A. E. (2015) · 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · doi:10.1109/icse.2015.91 · Cited by 364
El-Sappagh, S., Alonso, J. M., Islam, S. M. R., Sultan, A. M., & Kwak, K. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-82098-3 · Cited by 363
“FURIA is based on the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm 77 .”
MethodNested cross-validation when selecting classifiers is overzealous for most practical applications
Wainer, J., & Cawley, G. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115222 · Cited by 309
MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud1
Abbasi, A., Albrecht, C., Vance, A., & Hansen, J. (2012) · MIS Quarterly · doi:10.2307/41703508 · Cited by 298
k-NN as an implementation of situation testing for discrimination discovery and prevention
Luong, B. T., Ruggieri, S., & Turini, F. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020488 · Cited by 263
Turn-taking cues in task-oriented dialogue
Gravano, A., & Hirschberg, J. (2011) · Computer Speech & Language · doi:10.1016/j.csl.2010.10.003 · Cited by 254
Machine learning based encrypted traffic classification: Identifying SSH and Skype
Alshammari, R., & Zincir-Heywood, A. N. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356534 · Cited by 202
Combining EEG signal processing with supervised methods for Alzheimer’s patients classification
Fiscon, G., Weitschek, E., Cialini, A., Felici, G., Bertolazzi, P., De Salvo, S., Bramanti, A., Bramanti, P., & De Cola, M. C. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0613-y · Cited by 172
“We also test other classification methods, such as function-based, rule-based, naive-based, and Bayesian-based (e.g., RIPPER [54], SVM [55], the MultiLayer Perceptron [56]), whose performances are not satisfying and hence are not reported.”
MethodNarayanan, M., Chen, E., He, J., Kim, B., Gershman, S., & Doshi-Velez, F. (2018) · arXiv · doi:10.48550/arxiv.1802.00682 · Cited by 153
An Evaluation of the Human-Interpretability of Explanation
Lage, I., Chen, E., He, J., Narayanan, M., Kim, B., Gershman, S., & Doshi-Velez, F. (2019) · arXiv · doi:10.48550/arxiv.1902.00006 · Cited by 125
Bayesian networks for interpretable machine learning and optimization
Mihaljević, B., Bielza, C., & Larrañaga, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.138 · Cited by 123
Learning Certifiably Optimal Rule Lists for Categorical Data
Angelino, E., Larus-Stone, N., Alabi, D., Seltzer, M., & Rudin, C. (2017) · arXiv · doi:10.48550/arxiv.1704.01701 · Cited by 108
(2017) · doi:10.1145/3097983.3098047 · Cited by 104
Predicting link quality using supervised learning in wireless sensor networks
Wang, Y., Martonosi, M., & Peh, L.-S. (2007) · ACM SIGMOBILE Mobile Computing and Communications Review · doi:10.1145/1317425.1317434 · Cited by 101
FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection
Noto, K., Brodley, C., & Slonim, D. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-011-0234-x · Cited by 79
Alzheimer's disease patients classification through EEG signals processing
Fiscon, G., Weitschek, E., Felici, G., Bertolazzi, P., De Salvo, S., Bramanti, P., & De Cola, M. C. (2014) · 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · doi:10.1109/cidm.2014.7008655 · Cited by 57
Howard, C., Jordan, P., Di Eugenio, B., & Katz, S. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0071-y · Cited by 51
Classification in the Wild: The Science and Art of Transparent Decision Making
Katsikopoulos, K. V., Şimşek, Ö., Buckmann, M., & Gigerenzer, G. (2021) · doi:10.7551/mitpress/11790.001.0001 · Cited by 42
Automating image segmentation verification and validation by learning test oracles
Frounchi, K., Briand, L. C., Grady, L., Labiche, Y., & Subramanyan, R. (2011) · Information and Software Technology · doi:10.1016/j.infsof.2011.06.009 · Cited by 31
TraMiner: vision-based analysis of locomotion traces for cognitive assessment in smart-homes
Zolfaghari, S., Khodabandehloo, E., & Riboni, D. (2022) · Cognitive Computation · doi:10.1007/s12559-020-09816-3 · Cited by 29
Comparing local search with respect to genetic evolution to detect intrusions in computer networks
Neri, F. (2002) · Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512) · doi:10.1109/cec.2000.870301 · Cited by 24
(2017) · Educational Data Mining · Cited by 22
Fahmy, H., Pastore, F., Briand, L., & Stifter, T. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3569935 · Cited by 17
An Empirical Comparison of Interpretable Models to Post-Hoc Explanations
Mahya, P., & Fürnkranz, J. (2023) · AI · doi:10.3390/ai4020023 · Cited by 17
Trojan, A., Laurenzi, E., Jüngling, S., Roth, S., Kiessling, M., Atassi, Z., Kadvany, Y., Mannhart, M., Jackisch, C., Kullak-Ublick, G., et al. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1443987 · Cited by 10
Logic-Based Explainable and Incremental Machine Learning
Gupta, G., Wang, H., Basu, K., Shakerin, F., Salazar, E., Varanasi, S. C., Padalkar, P., & Dasgupta, S. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-35254-6_28 · Cited by 4
Metadata
- Authors
- Cohen, W. W.
- Venue
- Machine Learning Proceedings 1995
- Publisher
- Elsevier
- Published
- 1995
- Pages
- 115-123
- OpenAlex
- W1670263352
- Cited by
- 3,787
- References
- 1
- FWCI
- 87.50
- Type
- book chapter
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.