### Abstract

Attribute reduction is one of the main contributions in Rough Set Theory (RST) that tries to find all possible reducts by eliminating redundant attributes while maintaining the information of the problem in hand. In this paper, we propose a meta-heuristic approach called a Variable Neighbourhood Iterated Improvement Search (VNS-IIS) algorithm for attribute reduction. It is a combination of the variable neighbourhood search with the iterated search algorithm where two local search algorithms i.e. a random iterated local search and a sequential iterated local search algorithm are employed in a parallel strategy. In VNS-IIS, an improved solution will always be accepted. The proposed method has been tested on the 13 well-known datasets that are available in the UCI machine learning repository. Experimental results show that the VNS-IIS is able to obtain competitive results when compared with other approaches mentioned in the literature in terms of minimal reducts.

Original language | English |
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Pages (from-to) | 554-568 |

Number of pages | 15 |

Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Volume | 8886 |

Publication status | Published - 2014 |

### Fingerprint

### Keywords

- Attribute Reduction
- Iterated Search
- Variable Neighbourhood Search

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

**Variable neighbourhood iterated improvement search algorithm for attribute reduction problems.** / Arajy, Yahya Z.; Abdullah, Salwani; Kifah, Saif.

Research output: Contribution to journal › Article

}

TY - JOUR

T1 - Variable neighbourhood iterated improvement search algorithm for attribute reduction problems

AU - Arajy, Yahya Z.

AU - Abdullah, Salwani

AU - Kifah, Saif

PY - 2014

Y1 - 2014

N2 - Attribute reduction is one of the main contributions in Rough Set Theory (RST) that tries to find all possible reducts by eliminating redundant attributes while maintaining the information of the problem in hand. In this paper, we propose a meta-heuristic approach called a Variable Neighbourhood Iterated Improvement Search (VNS-IIS) algorithm for attribute reduction. It is a combination of the variable neighbourhood search with the iterated search algorithm where two local search algorithms i.e. a random iterated local search and a sequential iterated local search algorithm are employed in a parallel strategy. In VNS-IIS, an improved solution will always be accepted. The proposed method has been tested on the 13 well-known datasets that are available in the UCI machine learning repository. Experimental results show that the VNS-IIS is able to obtain competitive results when compared with other approaches mentioned in the literature in terms of minimal reducts.

AB - Attribute reduction is one of the main contributions in Rough Set Theory (RST) that tries to find all possible reducts by eliminating redundant attributes while maintaining the information of the problem in hand. In this paper, we propose a meta-heuristic approach called a Variable Neighbourhood Iterated Improvement Search (VNS-IIS) algorithm for attribute reduction. It is a combination of the variable neighbourhood search with the iterated search algorithm where two local search algorithms i.e. a random iterated local search and a sequential iterated local search algorithm are employed in a parallel strategy. In VNS-IIS, an improved solution will always be accepted. The proposed method has been tested on the 13 well-known datasets that are available in the UCI machine learning repository. Experimental results show that the VNS-IIS is able to obtain competitive results when compared with other approaches mentioned in the literature in terms of minimal reducts.

KW - Attribute Reduction

KW - Iterated Search

KW - Variable Neighbourhood Search

UR - http://www.scopus.com/inward/record.url?scp=84921746855&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84921746855&partnerID=8YFLogxK

M3 - Article

VL - 8886

SP - 554

EP - 568

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -