FSSC: an algorithm for classifying numerical data using fuzzy soft set theory

Bana Handaga, Bana Handaga and Herawan, Tutut and Mat Deris, Mustafa (2012) FSSC: an algorithm for classifying numerical data using fuzzy soft set theory. International Journal of Fuzzy System Applications, 2 (4). pp. 29-46. ISSN 2156-177X

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Official URL: http://dx.doi.org/10.4018/ijfsa.2012100102

Abstract

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.

Item Type:Article
Uncontrolled Keywords:algorithm; classification; fuzzy logic; fuzzy soft set theory; numerical data; soft set
Subjects:Q Science > QA Mathematics > QA75 Calculating machines
Divisions:Faculty of Computer Science and Information Technology > Department of Software Engineering
ID Code:3610
Deposited By:Normajihan Abd. Rahman
Deposited On:24 Jan 2017 15:21
Last Modified:24 Jan 2017 15:21

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