Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

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.

Anglický 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.

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",
  annote="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.",
  address="NEUVEDEN",
  chapter="103468",
  edition="NEUVEDEN",
  howpublished="print",
  institution="NEUVEDEN",
  number="3",
  volume="2013",
  year="2013",
  month="may",
  pages="214--218",
  publisher="NEUVEDEN",
  type="journal article - other"
}