Publication detail

PSO-based Constrained Imbalanced Data Classification

HLOSTA, M. STRÍŽ, R. ZENDULKA, J. HRUŠKA, T.

Original Title

PSO-based Constrained Imbalanced Data Classification

Type

article in a collection out of WoS and Scopus

Language

English

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.

Keywords

Data mining, imbalance classification, constraints, PSO, Genetic Algorithm

Authors

HLOSTA, M.; STRÍŽ, R.; ZENDULKA, J.; HRUŠKA, T.

RIV year

2013

Released

5. 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

Pages from

234

Pages to

239

Pages count

6

URL

https://www.fit.vut.cz/research/publication/10438/

BibTex

@inproceedings{BUT103558,
  author="Martin {Hlosta} and Rostislav {Stríž} and Jaroslav {Zendulka} and Tomáš {Hruška}",
  title="PSO-based Constrained Imbalanced Data Classification",
  booktitle="Proceedings of the Twelth International Conference on Informatics INFORMATICS'2013",
  year="2013",
  pages="234--239",
  publisher="The University of Technology Košice",
  address="Spišská Nová Ves",
  isbn="978-80-8143-127-2",
  url="https://www.fit.vut.cz/research/publication/10438/"
}