Weighted clone selection algorithm based on rough set theory

Jia Wu, Zhihua Cai, Xiaolin Chen, Meng Li, Bin Guo

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Clone selection is a new artificial intelligence technology, with self-organization, self-learning, selfrecognition, self-memory capacity. In the traditional clone selection algorithm for data classification, all the attributes for classification have the same influence, which affects its classification performance to some extent, given an appropriate weight for each attribute value can modify this imbalance. Accordingly this, proposes a weighted clone selection algorithm based on rough set to improve the performance of clone selection. In weighted clone selection algorithm attribute weights obtained directly from the training data using rough set theory, the attribute weights was used to test Data classification. Then verify the validity of the method by the experiments of UCI data sets.
Original languageEnglish
Pages (from-to)1333-1339
Number of pages7
JournalJournal of Software
Volume8
Issue number6
Publication statusPublished - 2013
Externally publishedYes

Keywords

  • Clone selection
  • Attribute weight
  • Rough set Theory
  • Classification

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