Detail publikace

PSO-based Constrained Imbalanced Data Classification

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

Originální název

PSO-based Constrained Imbalanced Data Classification

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Rok RIV

2013

Vydáno

5. 11. 2013

Nakladatel

The University of Technology Košice

Místo

Spišská Nová Ves

ISBN

978-80-8143-127-2

Kniha

Proceedings of the Twelth International Conference on Informatics INFORMATICS'2013

Strany od

234

Strany do

239

Strany počet

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/"
}