Abstract
Emerging Pattern (EP)-based classifiers are a type of new classifiers based on itemsets whose occurrence in one dataset varies significantly from that of another. These classifiers are very promising and have shown to perform comparably with some popular classifiers. In this paper, we conduct two experiments to study the noise tolerance of EP-based classifiers. A primary concern is to ascertain if overfitting occurs in them. Our results highlight the fact that the aggregating approach in constructing EP-based classifiers prevents them from overfitting. We further conclude that perfect training accuracy does not necessarily lead to overfitting of a classifier as long as there exists a suitable mechanism, such as an aggregating approach, to counterbalance any propensity to overfit.
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References
Bailey, J., Manoukian, T., Ramamohanarao, K.: Fast algorithms for mining emerging patterns. In: Proc. of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 39–50 (2002)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases. Department of Information and Computer Sciences. University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: Proc. of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 43–52 (1999)
Dong, G., Zhang, X., Wong, L., Li, J.: CAEP: Classification by aggregating emerging patterns. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 30–42. Springer, Heidelberg (1999)
Fan, H., Ramamohanarao, K.: An efficient single-scan algorithm for mining essential jumping emerging patterns for classification. In: Proc. of the 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 456–462 (2002)
Kadie, C.M.: SEER: Maximum Likelihood Regression For Learning-speed Curves. PhD thesis, University of Illinois at Urbana-Champaign (1995)
Langley, P., Sage, S.: Induction of selective Bayesian classifiers. In: Proc. of the 10th Conference on Uncertainty in Artificial Intelligence, pp. 399–406. Morgan Kaufmann, San Francisco (1994)
Li, J.: Mining Emerging Patterns to Construct Accurate and Efficient Classifiers. PhD thesis, The University of Melbourne (2001)
Li, J., Dong, G., Ramamohanarao, K.: Making use of the most expressive jumping emerging patterns for classification. In: Proc. of the 4th Pacific- Asia Conference on Knowledge Discovery and Data Mining, pp. 220–232 (2000)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proc. of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 80–86 (1998)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Sebag, M.: Delaying the choice of bias: A disjunctive version space approach. In: Proc. of the 13th International Conference on Machine Learning, pp. 444–452. Morgan Kaufmann, San Francisco (1996)
Zhang, X., Dong, G., Ramamohanarao, K.: Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets. In: Proc. of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 310–314 (2000)
Zhang, X., Dong, G., Ramamohanarao, K.: Information based classification by aggregating emerging patterns. In: Proc. of the 2nd International Conference on Intelligent Data Engineering and Automated Learning, pp. 48–53 (2000)
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Sun, Q., Zhang, X., Ramamohanarao, K. (2003). Noise Tolerance of EP-Based Classifiers. In: Gedeon, T.(.D., Fung, L.C.C. (eds) AI 2003: Advances in Artificial Intelligence. AI 2003. Lecture Notes in Computer Science(), vol 2903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24581-0_68
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DOI: https://doi.org/10.1007/978-3-540-24581-0_68
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