Abstract
Twin support vector machines (TWSVM) and the improved version of it, twin bounded support vector machines (TBSVM), are based on the idea of constructing two-class proximal hyperplanes placed at least unit relative distance away from the opposite class data samples. Both inherently possess outlier and noise sensitivity which is common in most of the traditional machine learning classification algorithms. Intuitionistic fuzzy twin support vector machines (IFTSVM), on the other hand, apply the concept of fuzzy membership determined in the same feature space as the mapped input. However, IFTSVM utilizes the fuzzy membership values only for those opposite class samples that violate the constraint that they have to be at a minimum of unit distance from the class proximal hyperplane. IFTSVM does not consider the proximal terms of the objective functions which also are affected by outliers and noise. This work studies incorporating intuitionistic fuzzy membership to the proximal terms of the objective functions that ensure the class hyperplanes are derived at minimal proximity. Furthermore, the proposed work employs cost-sensitive learning so that the error cost of each class is balanced even in case of datasets with high imbalance ratio. Experimental study proves the effectiveness of the proposed algorithm.
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Borah, P., Phukan, R., Chunka, C. (2022). Twin Support Vector Machines Classifier Based on Intuitionistic Fuzzy Number. In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds) Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-9873-6_34
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DOI: https://doi.org/10.1007/978-981-16-9873-6_34
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