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Additional info for Hybrid Artificial Intelligent Systems: 6th International Conference, HAIS 2011, Wroclaw, Poland, May 23-25, 2011, Proceedings, Part I

Example text

Metacost: A general method for making classifiers cost–sensitive. In: KDD 1999, pp. 155–164 (1999) 9. : Cost–sensitive learning by cost–proportionate example weighting. In: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), pp. 435–442 (2003) 10. : The class imbalance problem: a systematic study. Intelligent Data Analysis Journal 6(5), 429–450 (2002) 11. : Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research 19, 315–354 (2003) Imbalanced Classification: Common Approaches and Open Problems 9 12.

Furthermore, cost-sensitive learning solutions incorporating both the data and algorithmic level approaches assume higher misclassification costs with samples in the minority class and seek to minimise the high cost errors [8,29]. Regarding this, in this section we first introduce the main features of preprocessing techniques and next, we describe the cost-sensitive learning approach. 1 A. Fern´ andez, S. Garc´ıa, and F. Herrera Preprocessing Imbalanced Datasets: Resampling Techniques In the specialised literature, we can find some papers about resampling techniques studying the effect of changing class distribution to deal with imbalanced datasets where it has been empirically proved that, applying a preprocessing step in order to balance the class distribution, is usually a positive solution [5,30,31].

Kurzyński, M. ): HAIS 2011, Part I, LNAI 6678, pp. 22–29, 2011. e. a strong similarity relation in the universe U. However, two basic types of objects’ similarity should be distinguished: a/ Direct similarity of objects in the universe U; b/ Indirect similarity of objects’ representations in an auxiliary features’ space W. In the last case, it is assumed that there is given an univocal irreversible projection U →W such that projection of a family of similarity classes in U leads also to a partition of W into a family of pair-wise disjoint similarity classes.

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