Abstract
In the research area of decision tree, numerous researchers have been focusing on improving the predictive accuracy. However, obvious improvement can hardly be made until the introduction of the ensemble classifier. In this paper, we propose an Evolutionary Attribute-Oriented Ensemble Classifier (EAOEC) to improve the accuracy of sub-classifiers and at the same time maintain the diversity among them. EAOEC uses the idea of evolution to choose proper attribute subset for the building of every sub-classifier. To avoid the huge computation cost for the evolution, EAOEC uses the gini value gained during the construction of a sub-tree as the evolution basis to build the next sub-tree. Eventually, EAOEC classifier uses uniform weight voting to combine all sub-classifiers and experiments show that EAOEC can efficiently improve the predictive accuracy.
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References
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees, Wadsworth (1984)
Quinlan, J.R.: C4.5: Program for Machine Learning. Morgen Kaufmann Publisher, San Mateo (1993)
Mehta, M., Agrawal, R., Rissanen, J.: SLIQ: A Fast Scalable Classifier for Data Mining. In: EDBT, pp. 18–32 (1996)
Breiman, L.: Bagging Predictors. Machine Learning 24(1), 123–140 (1996)
Freund, Y., Schapire, R.E.: Experiments with a New Boosting Algorithm. In: ICML, pp. 148–156 (1996)
Kuncheva, L., Whitaker, C.J.: Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning 51(2), 181–207 (2003)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Wolpert, D., Macready, W.G.: An Efficient Method To Estimate Bagging’s Generalization Error. Machine Learning 35(1), 41–55 (1999)
Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)
Guerra-Salcedo, C., Whitley, D.: Genetic Approach to Feature Selection for Ensemble Creation. In: GECCO, pp. 236–243 (1999)
Opitz, D.W.: Feature Selection for Ensembles. In: AAAI/IAAI, pp. 379–384 (1999)
Rastogi, R., Shim, K.: PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning. Data Mining and Knowledge Discovery 4(4), 315–344 (2000)
Ho, T.K.: Random decision forests. In: ICDAR, pp. 278–292 (1995)
Cunningham, P., Carney, J.: Diversity versus Quality in Classification Ensembles Based on Feature Selection. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 109–116. Springer, Heidelberg (2000)
Mehta, M., Rissanen, J., Agrawal, R.: MDL-Based Decision Tree Pruning. In: KDD, pp. 216–221 (1995)
Kuncheva, L., Whitaker, C.J., Shipp, C.A., Duin, R.P.W.: Is Independence Good For Combining Classifiers? In: ICPR, pp. 2168–2171 (2000)
Shipp, C.A., Kuncheva, L.: Relationships Between Combination Methods And Measures of Diversity In Combining Classifiers. Information Fusion 3(2), 135–148 (2002)
Rokach, L.: Ensemble Methods for Classifiers. The Data Mining and Knowledge Discovery Handbook 2005, pp. 957–980 (2005)
Windeatt, T.: Diversity/Accuracy and Ensemble Classifier Design. In: ICPR(3), pp. 454–457 (2004)
Melville, P., Mooney, R.J.: Diverse ensembles for active learning. In: ICML (2004)
Xie, Z., Zhang, Q., Hsu, W., Lee, M.L.: Enhancing SNNB with Local Accuracy Estimation and Ensemble Techniques. In: Zhou, L.-z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 523–535. Springer, Heidelberg (2005)
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Lee, CI., Tsai, CJ., Ku, CW. (2006). An Evolutionary and Attribute-Oriented Ensemble Classifier. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751588_128
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DOI: https://doi.org/10.1007/11751588_128
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