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ROUGH SET REDUCTION OF ATTRIBUTES AND THEIR DOMAINS FOR NEURAL NETWORKS
Author(s) -
Jelonek Jacek,
Krawiec Krzysztof,
Slowiński Roman
Publication year - 1995
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1995.tb00036.x
Subject(s) - reduction (mathematics) , rough set , data reduction , artificial neural network , preprocessor , computer science , set (abstract data type) , pattern recognition (psychology) , data set , artificial intelligence , data mining , dimensionality reduction , machine learning , mathematics , geometry , programming language
This paper presents an empirical study of the use of the rough set approach to reduction of data for a neural network classifying objects described by quantitative and qualitative attributes. Two kinds of reduction are considered: reduction of the set of attributes and reduction of the domains of attributes. Computational tests were performed with five data sets having different character, for original and two reduced representations of data. The learning time acceleration due to data reduction is up to 4.72 times. The resulting increase of misclassification error does not exceed 11.06%. These promising results let us claim that the rough set approach is a useful tool for preprocessing of data for neural networks.