Publication Details
Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm
Stríž Rostislav, Ing. (DIFS FIT BUT)
Kupčík Jan, Ing. (DIFS FIT BUT)
Zendulka Jaroslav, doc. Ing., CSc. (DIFS FIT BUT)
Hruška Tomáš, prof. Ing., CSc. (DIFS FIT BUT)
Imbalanced data, classification, genetic algorithm, logistic regression
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.
@ARTICLE{FITPUB10277,
author = "Martin Hlosta and Rostislav Str\'{i}\v{z} and Jan Kup\v{c}\'{i}k and Jaroslav Zendulka and Tom\'{a}\v{s} Hru\v{s}ka",
title = "Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm",
pages = "214--218",
journal = "International Journal of Machine Learning and Computing",
volume = 2013,
number = 3,
year = 2013,
ISSN = "2010-3700",
language = "english",
url = "https://www.fit.vut.cz/research/publication/10277"
}