Please use this identifier to cite or link to this item: https://scholar.ptuk.edu.ps/handle/123456789/696
Title: Computer Numerical Control-PCB Drilling Machine with Efficient Path Planning
Authors: Mafarja, M
Eleyan, D
Issue Date: 2013
Publisher: International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 1(2) (2013).
Abstract: Feature selection is an important concept in rough set theory; it aims to determine a minimal subset of features that are jointly sufficient for preserving a particular property of the original data. This paper proposes an attribute reduction method that is based on Ant Colony Optimization algorithm and rough set theory as an evaluation measurement. The proposed method was tested on standard benchmark datasets. The results show that this algorithm performs well and competes other attribute reduction approaches in terms of the number of the selected features and the running tim
Description: Feature selection is an important concept in rough set theory; it aims to determine a minimal subset of features that are jointly sufficient for preserving a particular property of the original data. This paper proposes an attribute reduction method that is based on Ant Colony Optimization algorithm and rough set theory as an evaluation measurement. The proposed method was tested on standard benchmark datasets. The results show that this algorithm performs well and competes other attribute reduction approaches in terms of the number of the selected features and the running tim
URI: https://scholar.ptuk.edu.ps/handle/123456789/696
Appears in Collections:Applied science faculty

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