Journal article

HLOSTA Martin, STRÍŽ Rostislav, KUPČÍK Jan, ZENDULKA Jaroslav and HRUŠKA Tomáš. Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm. International Journal of Machine Learning and Computing. Singapore: of Computer Science and Information Technology Press, 2013, vol. 2013, no. 3, pp. 214-218. ISSN 2010-3700. Available from: http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=36&id=304
Publication language:english
Original title:Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm
Title (cs):Klasifikace rozsáhlých nevyvážených dat pomocí logistické regrese a genetického algoritmu s omezujícími podmínkami
Pages:214-218
Place:SG
Year:2013
URL:http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=36&id=304
Journal:International Journal of Machine Learning and Computing, Vol. 2013, No. 3, Singapore, SG
ISSN:2010-3700
URL:http://www.ijmlc.org/papers/305-K0018.pdf [PDF]
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Keywords
Imbalanced data, classification, genetic algorithm, logistic regression
Annotation
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.
BibTeX:
@ARTICLE{
   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 = {http://www.fit.vutbr.cz/research/view_pub.php?id=10277}
}

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