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

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Machine Learning and Computing

ISSN

2010-3700

e-ISSN

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Svazek periodika

2013

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

5

Strana od-do

214-218

Kód UT WoS článku

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EID výsledku v databázi Scopus

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