Mínimos quadrados para problemas de múltiplas classes envolvendo twin support vector machine e aplicações de mineração de dados
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2018-12-07
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Universidade Federal de Goiás
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Data mining is an emerging area due to the increasing amount of data available in a variety of fields. In this context twin support vector machine (TWSVM) has attracted the attention of several researchers. In this thesis, we developed a feature selector algorithm and an algorithm for multi-class problems based on TWSVM. This learning algorithm with ternary outputs {- 1,0,+1 } is based on the Vapnik support vector theory, and evaluates all training samples with a 1-×-1-×-rest structure during the decomposition phase. One of the main advantages of the proposed algorithm is the use of the least squares version for multi-class problems, where it is necessary to solve two systems of linear equations instead of two quadratic programming problems in TWSVM. We also implemented the principle of minimization of structural risk in order to improve the generalizability. The Sherman-Morisson-Woodbury formula is applied to reduce the complexity of the non-linear formulation of the algorithm. We also apply data mining techniques that combine the use of analytical technique with data mining algorithms in the classification of several samples. The developed framework could be an excellent tool for detecting different types of fraud, verifying if products were grown in organic or conventional systems, as well as tracing the region of origin of wine made from a given type of grape.
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LIMA, M. D. Mínimos quadrados para problemas de múltiplas classes envolvendo twin support vector machine e aplicações de mineração de dados. 2018. 127 f. Tese (Doutorado em Ciência da Computação em Rede) - Universidade Federal de Goiás, Goiânia, 2018.