FSSC: An Algorithm for Classifying Numerical Data Using Fuzzy Soft Set Theory

Introduced is a new algorithm for the classification of numerical data using the theory of fuzzy soft set, named Fuzzy Soft Set Classifier (FSSC). The algorithm uses the fuzzy approach in the pre-processing stage to obtain features, and similarity concept in the process of classification. It can be...

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Bibliographic Details
Published inInternational journal of fuzzy system applications Vol. 2; no. 4; pp. 29 - 46
Main Authors Handaga, Bana, Herawan, Tutut, Deris, Mustafa Mat
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.10.2012
Subjects
Algorithms
Classification
Datasets
Fuzzy set theory
Fuzzy sets
Machine learning
Real numbers
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ISSN2156-177X
2156-1761
DOI10.4018/ijfsa.2012100102

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Summary:Introduced is a new algorithm for the classification of numerical data using the theory of fuzzy soft set, named Fuzzy Soft Set Classifier (FSSC). The algorithm uses the fuzzy approach in the pre-processing stage to obtain features, and similarity concept in the process of classification. It can be applied not only to binary-valued datasets, but also be able to classify the data that consists of real numbers. Comparison tests on seven datasets from UCI Machine Learning Repository have been carried out. It is shown that the proposed algorithm provides better accuracy and higher accuracy as compared to the baseline algorithm using soft set theory.
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ISSN:2156-177X
2156-1761
DOI:10.4018/ijfsa.2012100102