TY - JOUR

T1 - Design of a qualitative classification model through fuzzy support vector machine with type-2 fuzzy expected regression classifier preset

AU - Wei, Yicheng

AU - Watada, Junzo

AU - Pedrycz, Witold

PY - 2016

Y1 - 2016

N2 - Methods of qualitative analysis, such as qualitative classification, have gained importance as an essential complement of existing quantitative analysis in numerous fields. Only a few models have been developed to deal with qualitative inputs in the form of type-2 fuzzy(T2F) sets properly, given that traditional defuzzification method like the Karnik-Mendel algorithm performs dimensionality reduction at the cost of loss of information. To improve the situation, we define the expected value and variance of T2F set in this paper. By using a combination of them, we transfer the vertical three-dimensional uncertainty of T2F set to horizontal range uncertainty without much distortion of information. Additionally, current classification models are unsuitable to the partial classification problem if an output is not fully assigned to a single class. We build a comprehensive qualitative classification model based on fuzzy support vector machine (FSVM) combined with type-2 fuzzy expected regression (FER) to solve the partial classification problem as mentioned. This classifier (i.e. FER-FSVM) makes it possible to achieve the discrimination of output while characterizing membership for each class in terms of multidimensional qualitative inputs (attributes) in the form of T2F sets. FER-FSVM also can self-learn the data structure and shift between FER or FSVM for classification automatically, thus largely improving the efficiency of the classification process. The new model is almost 7 times more efficient than FSVM, as shown by our empirical experiments.

AB - Methods of qualitative analysis, such as qualitative classification, have gained importance as an essential complement of existing quantitative analysis in numerous fields. Only a few models have been developed to deal with qualitative inputs in the form of type-2 fuzzy(T2F) sets properly, given that traditional defuzzification method like the Karnik-Mendel algorithm performs dimensionality reduction at the cost of loss of information. To improve the situation, we define the expected value and variance of T2F set in this paper. By using a combination of them, we transfer the vertical three-dimensional uncertainty of T2F set to horizontal range uncertainty without much distortion of information. Additionally, current classification models are unsuitable to the partial classification problem if an output is not fully assigned to a single class. We build a comprehensive qualitative classification model based on fuzzy support vector machine (FSVM) combined with type-2 fuzzy expected regression (FER) to solve the partial classification problem as mentioned. This classifier (i.e. FER-FSVM) makes it possible to achieve the discrimination of output while characterizing membership for each class in terms of multidimensional qualitative inputs (attributes) in the form of T2F sets. FER-FSVM also can self-learn the data structure and shift between FER or FSVM for classification automatically, thus largely improving the efficiency of the classification process. The new model is almost 7 times more efficient than FSVM, as shown by our empirical experiments.

KW - Fuzzy support vector machine (FSVM)

KW - Qualitative classification

KW - Type-2 fuzzy expected regression (FER)

KW - Type-2 fuzzy(T2F) sets

UR - http://www.scopus.com/inward/record.url?scp=84960427803&partnerID=8YFLogxK

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U2 - 10.1002/tee.22224

DO - 10.1002/tee.22224

M3 - Article

AN - SCOPUS:84960427803

JO - IEEJ Transactions on Electrical and Electronic Engineering

JF - IEEJ Transactions on Electrical and Electronic Engineering

SN - 1931-4973

ER -