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Author: search.filters.author.Benjathepanun, NunthikaHas files: No

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    Rule extraction for support vector machine using input space expansion
    (2011-12-01)
    Pitiranggon, Prasan
    ;
    Benjathepanun, Nunthika
    ;
    Banditvilai, Somsri
    ;
    Boonjing, Veera
    Fuzzy Rule-Based System (FRB) in the form of human comprehensible IF-THEN rules can be extracted from Support Vector Machine (SVM) which is regarded as a black-boxed system. We first prove that SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS) are equivalent indicating that SVM's decision can actually be represented by fuzzy IFTHEN rules. We then propose a rule extraction method based on kernel function firing strength and unbounded support vector space expansion. An advantage of our method is the guarantee that the number of final fuzzy IF-THEN rules is equal or less than the number of support vectors in SVM, and it may reveal human comprehensible patterns. We compare our method against SVM using popular benchmark data sets, and the results are comparable. © 2011 Springer-Verlag Berlin Heidelberg.
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    Detection of currency crises by a novel rule extraction method from support vector machine
    (2010-11-30)
    Pitiranggon, Prasan
    ;
    Banditvilai, Somsri
    ;
    Benjathepanun, Nunthika
    This study attempts to obtain a set of human comprehensible fuzzy if-then rules for the detection of currency crises from Support Vector Machine (SVM). SVM is used with explanatory variables known to be associated with currency crises to detect occurrences of currency crises. Fuzzy if-then rules are then obtained from the SVM through our novel rule extraction method which is called Support Vector Space Expansion (SVSE) method in order to unveil human comprehensible patterns behind SVM blackboxed system decision. The overall results of detection of currency crises of the fuzzy if-then rules are comparable to those from the SVM, and the if-then rules obtained may be used by financial experts to try to explain patterns of related financial statuses when currency crises occur, plus the if-then rules can also be easily incorporated into a software program using any popular computer language.
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    Fuzzy rules generation and extraction from support vector machine based on kernel function firing signals
    (2010-08-01)
    Pitiranggon, Prasan
    ;
    Benjathepanun, Nunthika
    ;
    Banditvilai, Somsri
    ;
    Boonjing, Veera
    Our study proposes an alternative method in building Fuzzy Rule-Based System (FRB) from Support Vector Machine (SVM). The first set of fuzzy IF-THEN rules is obtained through an equivalence of the SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS). The second set of rules is generated by combining the first set based on strength of firing signals of support vectors using Gaussian kernel. The final set of rules is then obtained from the second set through input scatter partitioning. A distinctive advantage of our method is the guarantee that the number of final fuzzy IF- THEN rules is not more than the number of support vectors in the trained SVM. The final FRB system obtained is capable of performing classification with results comparable to its SVM counterpart, but it has an advantage over the black-boxed SVM in that it may reveal human comprehensible patterns.