Computational Methods of Feature Selection

<p>Due to increasing demands for dimensionality reduction research on feature selection has deeply and widely expanded into many fields including computational statistics pattern recognition machine learning data mining and knowledge discovery. Highlighting current research issues <b>Computational Methods of Feature Selection</b> introduces the basic concepts and principles state-of-the-art algorithms and novel applications of this tool.</p><p>The book begins by exploring unsupervised randomized and causal feature selection. It then reports on some recent results of empowering feature selection including active feature selection decision-border estimate the use of ensembles with independent probes and incremental feature selection. This is followed by discussions of weighting and local methods such as the ReliefF family <i>k</i>-means clustering local feature relevance and a new interpretation of Relief. The book subsequently covers text classification a new feature selection score and both constraint-guided and aggressive feature selection. The final section examines applications of feature selection in bioinformatics including feature construction as well as redundancy- ensemble- and penalty-based feature selection. </p><p>Through a clear concise and coherent presentation of topics this volume systematically covers the key concepts underlying principles and inventive applications of feature selection illustrating how this powerful tool can efficiently harness massive high-dimensional data and turn it into valuable reliable information.</p>