Nonlinear great deluge algorithm for rough set attribute reduction

Najmeh Sadat Jaddi, Salwani Abdullah

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

The process of reducing the number of attributes from an information system is known as attribute reduction problem. The action of attribute reduction problem is an important step of pre-processing in data mining. In the attribute reduction process, reduction is performed with considerations for minimum information loss. Having a dataset with discrete attribute values, it is possible to find a minimal subset of the original attribute set in rough set theory. Great Deluge algorithm optimizes this problem by controlling the search space using lower boundary "Level". This paper presents a modification of Great Deluge algorithm for rough set attribute reduction wherein the "Level" is nonlinear. In the modified model, the "Level" is increased by a value that is calculated based on the quality of the current solution for each iteration. An alternative neighborhood structure assists the nonlinear Great Deluge to improve the quality of the method. The standard datasets available in a UCI machine-learning repository are employed to examine the proposed method. The accuracies of the classifications are investigated using ROSETTA. The performance of the proposed method is evaluated by comparing the results of nonlinear with linear Great Deluge algorithm and other available approaches in the literature. Statistical tests are performed to analyze the results. Experimental results show promising results of the proposed method compared to other available approaches in the literature.

Original languageEnglish
Pages (from-to)49-62
Number of pages14
JournalJournal of Information Science and Engineering
Volume29
Issue number1
Publication statusPublished - Jan 2013

Fingerprint

Set theory
set theory
statistical test
Rough set theory
Statistical tests
information system
Data mining
Learning systems
Information systems
learning
performance
Values
Processing
literature

Keywords

  • Attribute reduction
  • Classification
  • Nonlinear great deluge algorithm
  • Optimization
  • Rough set theory

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Library and Information Sciences
  • Human-Computer Interaction
  • Computational Theory and Mathematics

Cite this

Nonlinear great deluge algorithm for rough set attribute reduction. / Jaddi, Najmeh Sadat; Abdullah, Salwani.

In: Journal of Information Science and Engineering, Vol. 29, No. 1, 01.2013, p. 49-62.

Research output: Contribution to journalArticle

@article{b0faa43030ba41debc9821b1508de19e,
title = "Nonlinear great deluge algorithm for rough set attribute reduction",
abstract = "The process of reducing the number of attributes from an information system is known as attribute reduction problem. The action of attribute reduction problem is an important step of pre-processing in data mining. In the attribute reduction process, reduction is performed with considerations for minimum information loss. Having a dataset with discrete attribute values, it is possible to find a minimal subset of the original attribute set in rough set theory. Great Deluge algorithm optimizes this problem by controlling the search space using lower boundary {"}Level{"}. This paper presents a modification of Great Deluge algorithm for rough set attribute reduction wherein the {"}Level{"} is nonlinear. In the modified model, the {"}Level{"} is increased by a value that is calculated based on the quality of the current solution for each iteration. An alternative neighborhood structure assists the nonlinear Great Deluge to improve the quality of the method. The standard datasets available in a UCI machine-learning repository are employed to examine the proposed method. The accuracies of the classifications are investigated using ROSETTA. The performance of the proposed method is evaluated by comparing the results of nonlinear with linear Great Deluge algorithm and other available approaches in the literature. Statistical tests are performed to analyze the results. Experimental results show promising results of the proposed method compared to other available approaches in the literature.",
keywords = "Attribute reduction, Classification, Nonlinear great deluge algorithm, Optimization, Rough set theory",
author = "Jaddi, {Najmeh Sadat} and Salwani Abdullah",
year = "2013",
month = "1",
language = "English",
volume = "29",
pages = "49--62",
journal = "Journal of Information Science and Engineering",
issn = "1016-2364",
publisher = "Institute of Information Science",
number = "1",

}

TY - JOUR

T1 - Nonlinear great deluge algorithm for rough set attribute reduction

AU - Jaddi, Najmeh Sadat

AU - Abdullah, Salwani

PY - 2013/1

Y1 - 2013/1

N2 - The process of reducing the number of attributes from an information system is known as attribute reduction problem. The action of attribute reduction problem is an important step of pre-processing in data mining. In the attribute reduction process, reduction is performed with considerations for minimum information loss. Having a dataset with discrete attribute values, it is possible to find a minimal subset of the original attribute set in rough set theory. Great Deluge algorithm optimizes this problem by controlling the search space using lower boundary "Level". This paper presents a modification of Great Deluge algorithm for rough set attribute reduction wherein the "Level" is nonlinear. In the modified model, the "Level" is increased by a value that is calculated based on the quality of the current solution for each iteration. An alternative neighborhood structure assists the nonlinear Great Deluge to improve the quality of the method. The standard datasets available in a UCI machine-learning repository are employed to examine the proposed method. The accuracies of the classifications are investigated using ROSETTA. The performance of the proposed method is evaluated by comparing the results of nonlinear with linear Great Deluge algorithm and other available approaches in the literature. Statistical tests are performed to analyze the results. Experimental results show promising results of the proposed method compared to other available approaches in the literature.

AB - The process of reducing the number of attributes from an information system is known as attribute reduction problem. The action of attribute reduction problem is an important step of pre-processing in data mining. In the attribute reduction process, reduction is performed with considerations for minimum information loss. Having a dataset with discrete attribute values, it is possible to find a minimal subset of the original attribute set in rough set theory. Great Deluge algorithm optimizes this problem by controlling the search space using lower boundary "Level". This paper presents a modification of Great Deluge algorithm for rough set attribute reduction wherein the "Level" is nonlinear. In the modified model, the "Level" is increased by a value that is calculated based on the quality of the current solution for each iteration. An alternative neighborhood structure assists the nonlinear Great Deluge to improve the quality of the method. The standard datasets available in a UCI machine-learning repository are employed to examine the proposed method. The accuracies of the classifications are investigated using ROSETTA. The performance of the proposed method is evaluated by comparing the results of nonlinear with linear Great Deluge algorithm and other available approaches in the literature. Statistical tests are performed to analyze the results. Experimental results show promising results of the proposed method compared to other available approaches in the literature.

KW - Attribute reduction

KW - Classification

KW - Nonlinear great deluge algorithm

KW - Optimization

KW - Rough set theory

UR - http://www.scopus.com/inward/record.url?scp=84876276205&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876276205&partnerID=8YFLogxK

M3 - Article

VL - 29

SP - 49

EP - 62

JO - Journal of Information Science and Engineering

JF - Journal of Information Science and Engineering

SN - 1016-2364

IS - 1

ER -