Publication detail
PSO-based Constrained Imbalanced Data Classification
HLOSTA, M. STRÍŽ, R. ZENDULKA, J. HRUŠKA, T.
Original Title
PSO-based Constrained Imbalanced Data Classification
English Title
PSO-based Constrained Imbalanced Data Classification
Type
conference paper
Language
en
Original Abstract
The paper deals with classification of highly imbalanced data with accuracy constraints for the minority class. We solve this problem by our proposed meta-learning method that uses cost-sensitive logistic regression to generate initial candidate models. These models can be used as an initial solutions for various optimization algorithms. This paper is aimed for using Particle Swarm Optimization (PSO) to handle the constrained imbalanced classification problem. Experiments, comparing with Genetic Algorithm (GA), show that the swarm intelligence approach is suitable for this problem and outperforms GA.
English abstract
The paper deals with classification of highly imbalanced data with accuracy constraints for the minority class. We solve this problem by our proposed meta-learning method that uses cost-sensitive logistic regression to generate initial candidate models. These models can be used as an initial solutions for various optimization algorithms. This paper is aimed for using Particle Swarm Optimization (PSO) to handle the constrained imbalanced classification problem. Experiments, comparing with Genetic Algorithm (GA), show that the swarm intelligence approach is suitable for this problem and outperforms GA.
Keywords
Data mining, imbalance classification, constraints, PSO, Genetic Algorithm
RIV year
2013
Released
05.11.2013
Publisher
The University of Technology Košice
Location
Spišská Nová Ves
ISBN
978-80-8143-127-2
Book
Proceedings of the Twelth International Conference on Informatics INFORMATICS'2013
Edition
NEUVEDEN
Edition number
NEUVEDEN
Pages from
234
Pages to
239
Pages count
6
URL
BibTex
@inproceedings{BUT103558,
author="Martin {Hlosta} and Rostislav {Stríž} and Jaroslav {Zendulka} and Tomáš {Hruška}",
title="PSO-based Constrained Imbalanced Data Classification",
annote="The paper deals with classification of highly imbalanced data with accuracy
constraints for the minority class. We solve this problem by our proposed
meta-learning method that uses cost-sensitive logistic regression to generate
initial candidate models. These models can be used as an initial solutions for
various optimization algorithms. This paper is aimed for using Particle Swarm
Optimization (PSO) to handle the constrained imbalanced classification problem.
Experiments, comparing with Genetic Algorithm (GA), show that the swarm
intelligence approach is suitable for this problem and outperforms GA.",
address="The University of Technology Košice",
booktitle="Proceedings of the Twelth International Conference on Informatics INFORMATICS'2013",
chapter="103558",
edition="NEUVEDEN",
howpublished="print",
institution="The University of Technology Košice",
year="2013",
month="november",
pages="234--239",
publisher="The University of Technology Košice",
type="conference paper"
}