An Empirical Study of Oversampling and Undersampling Methods for LCMine an Emerging Pattern Based Classifier
作者： Octavio Loyola-González , Milton García-Borroto , Miguel Angel Medina-Pérez , José Fco. Martínez-Trinidad , Jesús Ariel Carrasco-Ochoa
DOI: 10.1007/978-3-642-38989-4_27
关键词:
摘要: Classifiers based on emerging patterns are usually more understandable for humans than those complex mathematical models. However, most of the classifiers get low accuracy in problems with imbalanced databases. This problem has been tackled through oversampling or undersampling methods, nevertheless, to best our knowledge these methods have not tested patterns. Therefore, this paper, we present an empirical study about use and improve a classifier We apply popular over 30 databases from UCI Repository Machine Learning. Our experimental results show that using significantly improves minority class.
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