Feature selection for automatic classification of musical instrument sounds
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Rough Sets as A Tool for Audio Signal Classification
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
The normal parameter reduction of soft sets and its algorithm
Computers & Mathematics with Applications
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
The parameterization reduction of soft sets and its applications
Computers & Mathematics with Applications
Feature Extraction for Traditional Malay Musical Instruments Classification System
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
A rough set approach for selecting clustering attribute
Knowledge-Based Systems
On multi-soft sets construction in information systems
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A soft set approach for association rules mining
Knowledge-Based Systems
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Soft set theory for feature selection of traditional Malay musical instrument sounds
ICICA'10 Proceedings of the First international conference on Information computing and applications
Data filling approach of soft sets under incomplete information
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
A new efficient normal parameter reduction algorithm of soft sets
Computers & Mathematics with Applications
Matrices representation of multi soft-sets and its application
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
A Study on Feature Analysis for Musical Instrument Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Feature Reduction with Inconsistency
International Journal of Cognitive Informatics and Natural Intelligence
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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.