Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F13%3APU106328" target="_blank" >RIV/00216305:26230/13:PU106328 - isvavai.cz</a>
Result on the web
<a href="http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=36&id=304" target="_blank" >http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=36&id=304</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Constrained Classification of Large Imbalanced Data by Logistic Regression and Genetic Algorithm
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Machine Learning and Computing
ISSN
2010-3700
e-ISSN
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Volume of the periodical
2013
Issue of the periodical within the volume
3
Country of publishing house
SG - SINGAPORE
Number of pages
5
Pages from-to
214-218
UT code for WoS article
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EID of the result in the Scopus database
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