DC programming and DCA for some classes of problems in machine learning and data mining

Abstract : Classification (supervised, unsupervised and semi-supervised) is one of important research topics of data mining which has many applications in various fields. In this thesis, we focus on developing optimization approaches for solving some classes of optimization problems in data classification. Firstly, for unsupervised learning, we considered and developed the algorithms for two well-known problems: the modularity maximization for community detection in complex networks and the data visualization problem with Self-Organizing Maps. Secondly, for semi-supervised learning, we investigated the effective algorithms to solve the feature selection problem in semi-supervised Support Vector Machine. Finally, for supervised learning, we are interested in the feature selection problem in multi-class Support Vector Machine. All of these problems are large-scale non-convex optimization problems. Our methods are based on DC Programming and DCA which are well-known as powerful tools in optimization. The considered problems were reformulated as the DC programs and then the DCA was used to obtain the solution. Also, taking into account the structure of considered problems, we can provide appropriate DC decompositions and the relevant choice strategy of initial points for DCA in order to improve its efficiency. All these proposed algorithms have been tested on the real-world datasets including biology, social networks and computer security
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Manh Cuong Nguyen. DC programming and DCA for some classes of problems in machine learning and data mining. Other [cs.OH]. Université de Lorraine, 2014. English. ⟨NNT : 2014LORR0080⟩. ⟨tel-01750803⟩

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