Abstract—Imbalance in data classification is a frequently discussed problem that is not well handled by classical classification techniques. The problem we tackled was to learn binary classification model from large data with accuracy constraint for the minority class. We propose a new meta-learning method that creates initial models using cost-sensitive learning by logistic regression and uses these models as initial chromosomes for genetic algorithm. The method has been successfully tested on a large real-world data set from our internet security research. Experiments prove that our method always leads to better results than usage of logistic regression or genetic algorithm alone. Moreover, this method produces easily understandable classification model.
Index Terms—Imbalanced data, classification, genetic algorithm, logistic regression.
The authors are with the Department of Information Systems, Faculty of Information Technology, IT4Innovations Centre of Excellence, Brno University of Technology, Božetěchova 1/2, 612 66 Brno, Czech Republic (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
Cite:Martin Hlosta, Rostislav Stríž, Jan Kupčík, Jaroslav Zendulka, and Tomáš Hruška, "Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm," International Journal of Machine Learning and Computing vol. 3, no. 2, pp. 214-218, 2013.