single-jc.php

JACIII Vol.4 No.1 pp. 31-38
doi: 10.20965/jaciii.2000.p0031
(2000)

Paper:

Using Rough Sets for Practical Feature Selection in a Rough Sets/Neural Network Framework for Knowledge Discovery

Ilona Jagielska

School of Information Management & Systems, Monash University PO BOX 197, 26 Sir John Monash Drive, Caulfield East, Victoria 3145, Australia

Received:
October 17, 1998
Accepted:
June 3, 1999
Published:
January 20, 2000
Keywords:
Rough sets, Neural networks, Feature selection, Knowledge discovery
Abstract

An important task in knowledge discovery is feature selection. This paper describes a practical approach to feature subset selection proposed as part of a hybrid rough sets/neural network framework for knowledge discovery for decision support. In this framework neural networks and rough sets are combined and used cooperatively during the system life cycle. The reason for combining rough sets with neural networks in the proposed framework is twofold. Firstly, rough sets based systems provide domain knowledge expressed in the form of If-then rules as well as tools for data analysis. Secondly, rough sets are used in this framework in the task of feature selection for neural network models. This paper examines the feature selection aspect of the framework. An empirical study that tested the approach on artificial datasets and real-world datasets was carried out. Experimental results indicate that the proposed approach can improve the performance of neural network models. The framework was also applied in the development of a real-world decision support system. The experience with this application has shown that the approach can support the users in the task of feature selection.

Cite this article as:
Ilona Jagielska, “Using Rough Sets for Practical Feature Selection in a Rough Sets/Neural Network Framework for Knowledge Discovery,” J. Adv. Comput. Intell. Intell. Inform., Vol.4, No.1, pp. 31-38, 2000.
Data files:

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 15, 2021