ゲノム情報科学研究教育機構  アブストラクト
Date June 5, 2013
Speaker Dr. Hongmin Cai, South China University of Technology
Title Feature Selection via Local Hyperplane Approximation
Abstract In this first talk, we will report a new feature weighting algorithm through maximizing a margin via local hyperplane approximation. The key idea is to estimate the feature weights through local approximation rather than global measurement, as used in previous methods. The proposed method can be categorized into the famous RELIEF framework. In particularly, the feature weights can be further estimated by minimizing leave-one-out cross validation error of classifier of HKNN. Therefore, optimal solution can be explicitly approximated to give feature estimation. The weights obtained by our method are more robust to degradation of noisy features, even when the number of dimensions is huge. Empirical study on both synthetic and real-world data sets demonstrate the superior performance of the feature selection for supervised learning, and the effectiveness of our algorithm. Several empirical researches on cancer related pathological problems are also reported.
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