Life Sciences Commons^™

Feature Selection Via Random Subsets Of Uncorrelated Features, Long Kim Dang

USF Tampa Graduate Theses and Dissertations

The role of feature selection is crucial in many applications. A few of these include computational biology, image classification and risk management. In biology, gene expression micro array data sets have been used extensively in many areas of research. These data sets typically suffer from an important problem: the ratio between the number of features over the number of examples is very high. This problem mainly affects prediction accuracy because it is best to collect more labeled examples than features. A correlation based random subspace ensemble feature selector (CCC_RSM) was proposed to handle this problem [5]. In this approach, first …