A Hybrid Particle Swarm Optimization and Fuzzy Rule-Based System for Breast Cancer Diagnosis

Najmeh Alikar, Salwani Abdullah, Seyed Mohsen Mousavi, Seyed Taghi Akhavan Niaki

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A hybrid algorithm of a particle swarm optimization and a fuzzy rule-based classification system is proposed in this study to diagnose breast cancer. Two orthogonal and triangular types of fuzzy sets are applied to represent the input variables. In additional, different input membership functions are considered to increase the classification accuracy. The performance of the proposed hybrid algorithm is studied using a classification accuracy measure on the Wisconsin breast cancer dataset. The results of the comparison using different training data sets show the higher performance of the proposed methodology.

Original languageEnglish
Pages (from-to)126-133
Number of pages8
JournalInternational Journal of Soft Computing
Volume8
Issue number2
DOIs
Publication statusPublished - 2013

Fingerprint

Fuzzy Rule-based Systems
Hybrid Optimization
Knowledge based systems
Fuzzy rules
Breast Cancer
Particle swarm optimization (PSO)
Particle Swarm Optimization
Hybrid Algorithm
Membership functions
Fuzzy sets
Fuzzy Rules
Membership Function
Fuzzy Sets
Triangular
High Performance
Methodology

Keywords

  • Breast cancer diagnosis
  • Fuzzy rule based
  • Particle swarm optimization
  • Taguchi Method
  • Wisconsin breast cancer dataset

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Modelling and Simulation

Cite this

A Hybrid Particle Swarm Optimization and Fuzzy Rule-Based System for Breast Cancer Diagnosis. / Alikar, Najmeh; Abdullah, Salwani; Mohsen Mousavi, Seyed; Taghi Akhavan Niaki, Seyed.

In: International Journal of Soft Computing, Vol. 8, No. 2, 2013, p. 126-133.

Research output: Contribution to journalArticle

Alikar, Najmeh ; Abdullah, Salwani ; Mohsen Mousavi, Seyed ; Taghi Akhavan Niaki, Seyed. / A Hybrid Particle Swarm Optimization and Fuzzy Rule-Based System for Breast Cancer Diagnosis. In: International Journal of Soft Computing. 2013 ; Vol. 8, No. 2. pp. 126-133.
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