A discrete particle swarm for multi-objective problems in polynomial neural networks used for classification: A data mining perspective

Satchidananda Dehuri, Carlos A. Coello Coello, Sung Bae Cho, Ashish Ghosh

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Approximating decision boundaries of large datasets to classify an unknown sample has been recognized by many researchers within the data mining community as a very promising research topic. The application of polynomial neural networks (PNNs) for the approximation of decision boundaries can be considered as a multiple criteria problem rather than as one involving a single criterion. Classification accuracy and architectural complexity can be thought of as two different conflicting objectives when using PNNs for classification tasks. Using these two metrics as the objectives for finding decision boundaries, this chapter adopts a Discrete Pareto Particle Swarm Optimization (DPPSO) method. DPPSO guides the evolution of the swarm by using the two aforementioned objectives: classification accuracy and architectural complexity. The effectiveness of this method is shown on real life datasets having non-linear class boundaries. Empirical results indicate that the performance of the proposed method is encouraging.

Original languageEnglish
Title of host publicationSwarm Intelligence for Multi-objective Problems in Data Mining
EditorsCarlos Artemio Coello Coello, Satchidananda Dehuri, Susmita Ghosh
Pages115-155
Number of pages41
DOIs
Publication statusPublished - 2009 Oct 14

Publication series

NameStudies in Computational Intelligence
Volume242
ISSN (Print)1860-949X

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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    Dehuri, S., Coello Coello, C. A., Cho, S. B., & Ghosh, A. (2009). A discrete particle swarm for multi-objective problems in polynomial neural networks used for classification: A data mining perspective. In C. A. Coello Coello, S. Dehuri, & S. Ghosh (Eds.), Swarm Intelligence for Multi-objective Problems in Data Mining (pp. 115-155). (Studies in Computational Intelligence; Vol. 242). https://doi.org/10.1007/978-3-642-03625-5_6