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University of Tennessee, Knoxville

2010

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Kernel-Based Data Mining Approach With Variable Selection For Nonlinear High-Dimensional Data, Seung Hyun Baek May 2010

Kernel-Based Data Mining Approach With Variable Selection For Nonlinear High-Dimensional Data, Seung Hyun Baek

Doctoral Dissertations

In statistical data mining research, datasets often have nonlinearity and high-dimensionality. It has become difficult to analyze such datasets in a comprehensive manner using traditional statistical methodologies. Kernel-based data mining is one of the most effective statistical methodologies to investigate a variety of problems in areas including pattern recognition, machine learning, bioinformatics, chemometrics, and statistics. In particular, statistically-sophisticated procedures that emphasize the reliability of results and computational efficiency are required for the analysis of high-dimensional data. In this dissertation, first, a novel wrapper method called SVM-ICOMP-RFE based on hybridized support vector machine (SVM) and recursive feature elimination (RFE) with information-theoretic ...