Abstract
In many application domains, classification tasks have to tackle multiclass imbalanced training sets. We have been looking for a CBA approach (Classification Based on Association rules) in such difficult contexts. Actually, most of the CBA-like methods are one-vs-all approaches (OVA), i.e., selected rules characterize a class with what is relevant for this class and irrelevant for the union of the other classes. Instead, our method considers that a rule has to be relevant for one class and irrelevant for every other class taken separately. Furthermore, a constrained hill climbing strategy spares users tuning parameters and/or spending time in tedious post-processing phases. Our approach is empirically validated on various benchmark data sets.
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References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules Between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM Press, New York (1993)
Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Proceedings of the Fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 80–86. AAAI Press, Menlo Park (1998)
Bayardo, R., Agrawal, R.: Mining the Most Interesting Rules. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 145–154. ACM Press, New York (1999)
Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In: Proceedings of the First IEEE International Conference on Data Mining, pp. 369–376. IEEE Computer Society, Los Alamitos (2001)
Boulicaut, J.F., Crémilleux, B.: Simplest Rules Characterizing Classes Generated by Delta-free Sets. In: Proceedings of the Twenty-Second Annual International Conference Knowledge Based Systems and Applied Artificial Intelligence, pp. 33–46. Springer, Heidelberg (2002)
Yin, X., Han, J.: CPAR: Classification Based on Predictive Association Rules. In: Proceedings of the Third SIAM International Conference on Data Mining, pp. 369–376. SIAM, Philadelphia (2003)
Baralis, E., Chiusano, S.: Essential Classification Rule Sets. ACM Transactions on Database Systems 29(4), 635–674 (2004)
Bouzouita, I., Elloumi, S., Yahia, S.B.: GARC: A New Associative Classification Approach. In: Proceedings of the Eight International Conference on Data Ware-housing and Knowledge Discovery, pp. 554–565. Springer, Heidelberg (2006)
Freitas, A.A.: Understanding the Crucial Differences Between Classification and Discovery of Association Rules – A Position Paper. SIGKDD Explorations 2(1), 65–69 (2000)
Wang, J., Karypis, G.: HARMONY: Efficiently Mining the Best Rules for Classification. In: Proceedings of the Fifth SIAM International Conference on Data Mining, pp. 34–43. SIAM, Philadelphia (2005)
Arunasalam, B., Chawla, S.: CCCS: A Top-down Associative Classifier for Imbalanced Class Distribution. In: Proceedings of the Twelveth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 517–522. ACM Press, New York (2006)
Verhein, F., Chawla, S.: Using Significant Positively Associated and Relatively Class Correlated Rules for Associative Classification of Imbalanced Datasets. In: Proceedings of the Seventh IEEE International Conference on Data Mining, pp. 679–684. IEEE Computer Society Press, Los Alamitos
Dong, G., Li, J.: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 43–52. ACM Press, New York (1999)
Ramamohanarao, K., Fan, H.: Patterns Based Classifiers. World Wide Web 10(1), 71–83 (2007)
Cerf, L., Gay, D., Selmaoui, N., Boulicaut, J.F.: Technical Notes on fitcare’s Implementation. Technical report, LIRIS (April 2008)
Coenen, F.: The LUCS-KDD software library (2004), http://www.csc.liv.ac.uk/~frans/KDD/Software/ .
Newman, D., Hettich, S., Blake, C., Merz, C.: UCI Repository of Machine Learning Databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
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Cerf, L., Gay, D., Selmaoui, N., Boulicaut, JF. (2008). A Parameter-Free Associative Classification Method. In: Song, IY., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2008. Lecture Notes in Computer Science, vol 5182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85836-2_28
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DOI: https://doi.org/10.1007/978-3-540-85836-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85835-5
Online ISBN: 978-3-540-85836-2
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