TAILIEUCHUNG - Algorithm to build fuzzy decision tree for data classification problem based on fuzziness intervals matching

The precise data classification cannot solve all the requirements. Thus, the fuzzy decision tree classification problem is important for the fuzzy data mining problem. The fuzzy decision classification based on the fuzzy set theory has some limitations derived from its innerself. The hedge algebra with many advantages has become a really useful tool for solving the fuzzy decision tree classification. | Journal of Computer Science and Cybernetics, , (2016), 367–380 DOI ALGORITHM TO BUILD FUZZY DECISION TREE FOR DATA CLASSIFICATION PROBLEM BASED ON FUZZINESS INTERVALS MATCHING LE VAN TUONG LAN1 , NGUYEN MAU HAN,1 NGUYEN CONG HAO2 1 Information Technology Faculty, Hue University of Sciences, Hue University, VietNam lvtlan@, nmhan2009@ 2 Department of Testing, Hue University, VietNam; nchao@ Abstract. Nowadays, with the demand to reflect the real world, we have a number of imprecise stored business data warehouses. The precise data classification cannot solve all the requirements. Thus, the fuzzy decision tree classification problem is important for the fuzzy data mining problem. The fuzzy decision classification based on the fuzzy set theory has some limitations derived from its innerself. The hedge algebra with many advantages has become a really useful tool for solving the fuzzy decision tree classification. However, the sample data homogenising process based on the quantitative methods of the hedge algebra still has some restrictions because of errors evolved and the resulting tree is not truly flexible. So, the fuzzy decision tree obtained is not always highly predictable. In this paper, using fuzziness intervals matching with hedge algebra, the authors proposed an inductive learning method “ fuzzy decision tree” to obtain a fuzzy decision tree with high predictability. Keywords. Hedge algebra, data mining, fuzziness intervals matching, fuzzy decision tree, . 1. INTRODUCTION The real world is infinite while our language is limited, and there inevitably appear phrases that are inexact or ambiguous. Therefore, in practice, the business data warehouses fuzzily stored are inevitable, so the precise data classification can not solve all the requirements. The fuzzy classification problem has been studied by many scientists with different approaches [1–3, 6, 17, 18, 20–23, 25–27],

• Kỹ thuật lập trình
• Journal of Computer Science and Cybernetics
• Algorithm to build fuzzy decision tree for data classification problem
• Fuzziness intervals matching
• Fuzzy decision tree
• The fuzzy data mining problem
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