Detail publikace

Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm

HLOSTA, M. STRÍŽ, R. KUPČÍK, J. ZENDULKA, J. HRUŠKA, T.

Originální název

Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Imbalanced data, classification, genetic algorithm, logistic regression

Autoři

HLOSTA, M.; STRÍŽ, R.; KUPČÍK, J.; ZENDULKA, J.; HRUŠKA, T.

Rok RIV

2013

Vydáno

18. 5. 2013

ISSN

2010-3700

Periodikum

International Journal of Machine Learning and Computing

Ročník

2013

Číslo

3

Stát

Singapurská republika

Strany od

214

Strany do

218

Strany počet

5

URL

http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=36&id=304

BibTex

@article{BUT103468,
  author="Martin {Hlosta} and Rostislav {Stríž} and Jan {Kupčík} and Jaroslav {Zendulka} and Tomáš {Hruška}",
  title="Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm",
  journal="International Journal of Machine Learning and Computing",
  year="2013",
  volume="2013",
  number="3",
  pages="214--218",
  issn="2010-3700",
  url="http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=36&id=304"
}