Publication detail
Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm
HLOSTA, M. STRÍŽ, R. KUPČÍK, J. ZENDULKA, J. HRUŠKA, T.
Original Title
Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm
English Title
Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm
Type
journal article - other
Language
en
Original 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.
English 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.
Keywords
Imbalanced data, classification, genetic algorithm, logistic regression
RIV year
2013
Released
18.05.2013
Publisher
NEUVEDEN
Location
NEUVEDEN
ISBN
2010-3700
Periodical
International Journal of Machine Learning and Computing
Year of study
2013
Number
3
State
SG
Pages from
214
Pages to
218
Pages count
5
URL
Documents
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"
}