Abstract
When training classifiers, presence of noise can severely harm their performance. In this paper, we focus on “non-class” attribute noise and we consider how a frequent fault-tolerant (FFT) pattern mining task can be used to support noise-tolerant classification. Our method is based on an application independent strategy for feature construction based on the so-called δ-free patterns. Our experiments on noisy training data shows accuracy improvement when using the computed features instead of the original ones.
This work is partly funded by French ANR contract MDCO-2007 Bingo2.
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References
Zhu, X., Wu, X.: Class noise vs. attribute noise : A quantitative study. Artificial Intelligence Revue 22, 177–210 (2004)
Rebbapragada, U., Brodley, C.E.: Class noise mitigation through instance weighting. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS, vol. 4701, pp. 708–715. Springer, Heidelberg (2007)
Kubica, J., Moore, A.W.: Probabilistic noise identification and data cleaning. In: Proceedings ICDM 2003, pp. 131–138. IEEE Computer Society, Los Alamitos (2003)
Zhang, Y., Wu, X.: Noise modeling with associative corruption rules. In: Proceedings ICDM 2007, pp. 733–738. IEEE Computer Society, Los Alamitos (2007)
Yang, Y., Wu, X., Zhu, X.: Dealing with predictive-but-unpredictable attributes in noisy data sources. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS, vol. 3202, pp. 471–483. Springer, Heidelberg (2004)
Yang, C., Fayyad, U.M., Bradley, P.S.: Efficient discovery of error-tolerant frequent itemsets in high dimensions. In: Proceedings KDD 2001, pp. 194–203. ACM Press, New York (2001)
Besson, J., Pensa, R.G., Robardet, C., Boulicaut, J.F.: Constraint-based mining of fault-tolerant patterns from boolean data. In: Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol. 3933, pp. 55–71. Springer, Heidelberg (2006)
Pensa, R.G., Robardet, C., Boulicaut, J.F.: Supporting bi-cluster interpretation in 0/1 data by means of local patterns. Intelligent Data Analysis 10, 457–472 (2006)
Boulicaut, J.F., Bykowski, A., Rigotti, C.: Approximation of frequency queries by means of free-sets. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS, vol. 1910, pp. 75–85. Springer, Heidelberg (2000)
Selmaoui, N., Leschi, C., Gay, D., Boulicaut, J.F.: Feature construction and delta-free sets in 0/1 samples. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds.) DS 2006. LNCS, vol. 4265, pp. 363–367. Springer, Heidelberg (2006)
Gay, D., Selmaoui, N., Boulicaut, J.F.: Feature construction based on closedness properties is not that simple. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS, vol. 5012, pp. 112–123. Springer, Heidelberg (2008)
Cheng, H., Yan, X., Han, J., Hsu, C.W.: Discriminative frequent pattern analysis for effective classification. In: Proceedings ICDE 2007, pp. 716–725. IEEE Computer Society, Los Alamitos (2007)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings KDD 1998, pp. 80–86. AAAI Press, Menlo Park (1998)
Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings KDD 1999, pp. 43–52. ACM Press, New York (1999)
Ramamohanarao, K., Fan, H.: Patterns based classifiers. World Wide Web 10, 71–83 (2007)
Fan, H., Ramamohanarao, K.: Noise tolerant classification by chi emerging patterns. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS, vol. 3056, pp. 201–206. Springer, Heidelberg (2004)
Bastide, Y., Taouil, R., Pasquier, N., Stumme, G., Lakhal, L.: Mining frequent patterns with counting inference. SIGKDD Explorations 2, 66–75 (2000)
Crémilleux, B., Boulicaut, J.F.: Simplest rules characterizing classes generated by delta-free sets. In: Proceedings ES 2002, pp. 33–46. Springer, Heidelberg (2002)
Hébert, C., Crémilleux, B.: Optimized rule mining through a unified framework for interestingness measures. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 238–247. Springer, Heidelberg (2006)
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continous-valued attributes for classification learning. In: Proceedings IJCAI 1993, pp. 1022–1027. Morgan Kaufmann, San Francisco (1993)
Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Cerf, L., Gay, D., Selmaoui, N., Boulicaut, J.F.: A parameter free associative classifier. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 293–304. Springer, Heidelberg (2008)
Zhang, S., Wu, X., Zhang, C., Lu, J.: Computing the minimum-support for mining frequent patterns. Knowledge and Information Systems 15, 233–257 (2008)
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Gay, D., Selmaoui, N., Boulicaut, JF. (2009). Application-Independent Feature Construction from Noisy Samples. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_102
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DOI: https://doi.org/10.1007/978-3-642-01307-2_102
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