Rough and soft set approaches for attributes selection of traditional Malay musical instrument sounds classification

Mohd Nawi, Nazri and Senan, Norhalina and Ibrahim, Rosziati and Yanto, Iwan Tri Riyadi and Herawan, Tutut (2012) Rough and soft set approaches for attributes selection of traditional Malay musical instrument sounds classification. International Journal of Software Science and Computational Intelligence, 4 (2). pp. 14-40. ISSN 1942-9045

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Feature selection or attribute reduction is performed mainly to avoid the 'curse of dimensionality' in the large database problem including musical instrument sound classification. This problem deals with the irrelevant and redundant features. Rough set theory and soft set theory proposed by Pawlak and Molodtsov, respectively, are mathematical tools for dealing with the uncertain and imprecision data. Rough and soft set-based dimensionality reduction can be considered as machine learning approaches for feature selection. In this paper, the authors applied these approaches for data cleansing and feature selection technique of Traditional Malay musical instrument sound classification. The data cleansing technique is developed based on matrices computation of multi-soft sets while feature selection using maximum attributes dependency based on rough set theory. The modeling process comprises eight phases: data acquisition, sound editing, data representation, feature extraction, data discretization, data cleansing, feature selection, and feature validation via classification. The results show that the highest classification accuracy of 99.82% was achieved from the best 17 features with 1-NN classifier.

Item Type:Article
Uncontrolled Keywords:data cleansing; feature selection; rough set theory; soft set theory; traditional Malay musical instrument sound dataset
Subjects:Q Science > QA Mathematics > QA75 Calculating machines
Divisions:Faculty of Computer Science and Information Technology > Department of Multimedia
ID Code:3609
Deposited By:Normajihan Abd. Rahman
Deposited On:24 Jan 2017 15:19
Last Modified:24 Jan 2017 15:19

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