Abstract
The performance of traditional classifier skews towards the majority class for imbalanced data, resulting in high misclassification rate for minority samples. To solve this problem, a combination classification algorithm based on outlier detection and C4.5 is presented. The basic idea of the algorithm is to make the data distribution balance by grouping the whole data into rare clusters and major clusters through the outlier factor. Then C4.5 algorithm is implemented to build the decision trees on both the rare clusters and the major clusters respectively. When classifying a new object, the decision tree for evaluation will be chosen according to the type of the cluster which the new object is nearest. We use the datasets from the UCI Machine Learning Repository to perform the experiments and compare the effects with other classification algorithms; the experiments demonstrate that our algorithm performs much better for the extremely imbalanced data sets.
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References
Weiss, G.M.: Mining with Rarity: A Uinfying Framework. Sigkdd Explorations 6(1), 7–19 (2004)
Marcus, A.: Learning when data set s are imbalanced and when costs are unequal and unknown. In: Proc. of t he Workshop on Learning from Imbalanced Data Sets II, ICML, Washington DC (2003)
Liu, X.-Y., Wu, J., Zhou, Z.-H.: Exploratory Undersampling for Class-Imbalance Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(2), 539–550 (2009)
Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning. LNCS, pp. 878–887. Springer, Heidelberg (2005)
Guo, H., Viktor, H.L.: Learning from Imbalanced Data Set s with Boosting and Data Generation: The DataBoost-IM Approach. Sigkdd Explorations 6, 30–39 (2003)
Hong, X., Chen, S., Harris, C.J.: A Kernel-Based Two-Class Classifier for Imbalanced Data Sets. IEEE Transactions on Neural Networks 17(6), 786–795 (2007)
Su, C.-T., Chen, L.-S., Yih, Y.: Knowledge acquisition through information granulation for imbalanced data. Expert Systems with applications 31, 531–541 (2006)
Jiang, S., Song, X.: A clustering-based method for unsupervised intrusion detections. Pattern Recognition Letters 5, 802–810 (2006)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
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Jiang, S., Yu, W. (2009). A Combination Classification Algorithm Based on Outlier Detection and C4.5. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_50
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DOI: https://doi.org/10.1007/978-3-642-03348-3_50
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