Abstract
When different subsamples of the same data set are used to induce classification trees, the structure of the built classifiers is very different. The stability of the structure of the tree is of capital importance in many domains, such as illness diagnosis, fraud detection in different fields, customer’s behaviour analysis (marketing), etc, where comprehensibility of the classifier is necessary. We have developed a methodology for building classification trees from multiple samples where the final classifier is a single decision tree (Consolidated Trees). The paper presents an analysis of the structural stability of our algorithm versus C4.5 algorithm. The classification trees generated with our algorithm, achieve smaller error rates and structurally more steady trees than C4.5 when using resampling techniques. The main focus on this paper is showing how Consolidated Trees built with different sets of subsamples tend to converge to the same tree when the number of used subsamples is increased.
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References
Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36, 105–139 (1999)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases, University of California, Irvine, Dept. of Information and Computer Sciences (1998), http://www.ics.uci.edu/~mlearn.MLRepository.html
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Chan, P.K., Stolfo, S.J.: Toward Scalable Learning with Non-uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 164–168 (1998)
Dietterich, T.G.: Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation 10(7), 1895–1924 (1998)
Dietterich, T.G.: An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization. Machine Learning 40, 139–157 (2000)
Domingos, P.: Knowledge acquisition from examples via multiple models. In: Proc. 14th International Conference on Machine Learning Nashville, TN, pp. 98–106 (1997)
Drummond, C., Holte, R.C.: Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria. In: Proceedings of the 17th International Conference on Machine Learning, pp. 239–246 (2000)
Elkan, C.: The Foundations of Cost-Sensitive Learning. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, pp. 973–978 (2001)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: Proceedings of the 13th International Conference on Machine Learning, pp. 148–156 (1996)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, Heidelberg (2001) ISBN: 0-387-95284-5
Japkowicz, N.: Learning from Imbalanced Data Sets: A Comparison of Various Strategies. In: Proceedings of the AAAI Workshop on Learning from Imbalanced Data Sets, Menlo Park, CA (2000)
Pérez, J.M., Muguerza, J., Arbelaitz, O., Gurrutxaga, I.: A new algorithm to build consolidated trees: study of the error rate and steadiness. In: Proceedings of the conference on Intelligent Information Systems, Zakopane, Poland (2004)
Pérez, J.M., Muguerza, J., Arbelaitz, O., Gurrutxaga, I., Martín, J.I.: Behaviour of Consolidated Trees when using Resampling Techniques. In: Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems, PRIS, Porto, Portugal (2004)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc. (eds.), San Mateo (1993)
Skurichina, M., Kuncheva, L.I., Duin, R.P.W.: Bagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 62–71. Springer, Heidelberg (2002)
Turney, P.: Bias and the quantification of stability. Machine Learning 20, 23–33 (1995)
Weiss, G.M., Provost, F.: Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction. Journal of Artificial Intelligence Research 19, 315–354 (2003)
Windeatt, T., Ardeshir, G.: Boosted Tree Ensembles for Solving Multiclass Problems. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 42–51. Springer, Heidelberg (2002)
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Pérez, J.M., Muguerza, J., Arbelaitz, O., Gurrutxaga, I., Martín, J.I. (2006). Consolidated Trees: An Analysis of Structural Convergence. In: Williams, G.J., Simoff, S.J. (eds) Data Mining. Lecture Notes in Computer Science(), vol 3755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677437_4
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DOI: https://doi.org/10.1007/11677437_4
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