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A commonly used approach for feature selection is to select those features that extremize certain probabilistic distance measures. In most of the procedures it is assumed that the labels of the patterns are perfect. There are many practical situations in which the labels of the patterns are imperfect. This paper examines the applicability of the extremization of the Bhattacharyya distance, the divergence, equivocation, Kalmogrov variational distance, and Matusita distance as criteria for selecting the effective features from imperfectly identified patterns.

Walsh functions have become quite useful in the applications of image processing and feature selection. Due to their inherent efficiency of implementation, (they are a subset of the Reed Muller Codes and Hadamard matrices), they have become popular for coding, enhancement and other signal processing tasks. This paper will briefly describe applications in some of these areas with emphasis on a correlation analysis for justification purposes.

This paper gives a unified approach to designing a data analyzer that performs cluster-seeking, feature selection, and categorizer design under a weighted least-square performance criterion. The cost of misrecognitions is preserved throughout the process, It can be used as a fast procedure to evaluate the discriminatory capability of sensors and/or preprocessors.

This paper proposes the concept of equivocation from information theory as a criterion for feature selection. An expression for equivocation is derived in terms of class conditional probabilities. By making the assumption that the patterns in each class are normally distributed, a transformation matrix that minimizes the equivocation in the reduced dimension is obtained. Furthermore, a relationship between the equivocation and the expected divergence between any pair of classes is presented.

There are a number of variable selection procedures in current use in multiple regression analysis. In some of these procedures, a new variable is entered into regression when its contribution to the explanation of the response is larger than that of any other variable not yet entered. The new entrant is then tested by a standard F-test to see if its contribution is 'significant'. Because of the method of selecting the largest contributor, an F-test of this type cannot be theoretically correct but the procedure is used because the exact distributions are not known. This raises questions like 'Is one seriously in error in using the F-tests.' 'If so, can adjustments be made to the F-test to correct the difficulty.' The purpose of this paper is to discuss some new results which throw light on these questions. (Author)

In this paper a quite general formulation of sequential pattern recognition processes is presented. Within the framework of this formulation, a procedure is obtained for the simultaneous optimization of the stopping rule and the stage-by-stage ordering of features as the process proceeds. This optimization procedure is based on dynamic programming and uses as an index of performance the expected cost of the process, including both the cost of feature measurement and the cost of classification errors. A simple example illustrates the important computational aspects of the procedure and indicates the form of the solution.

This paper presents support vector machine based methods for arrhythmia classification in ECG datasets with selected features. Among various existing SVM methods, four well-known and widely used algorithms One Against One (OAO), One Against All (OAA), Fuzzy Decision Function (FDF) and Decision Directed Acyclic Graph (DDAG) are used here to distinguish between the presence and absence of cardiac arrhythmia and classifying them into one of the arrhythmia groups. The various types of arrhythmias in the cardiac arrhythmias ECG database chosen from University of California at Irvine (UCI) to train SVM include ischemic changes (coronary artery disease), old inferior myocardial infarction, sinus bradycardy, right bundle branch block, and others. ECG arrhythmia datasets are of generally complex nature and the results obtained through implementation of four well known methods are thus compared as per their accuracy rate in percentages and the performance of the SVM classifier using One Against All (OAA) technique was found to be of vital importance for classification based diagnosing diseases pertaining to abnormal heart beats. We have also used feature selection on the ECG datasets using Principal Component Analysis (PCA) method. It has been observed that after applying feature selection and then performing classification of ECG datasets through SVM based methods, One Against All (OAA) gives better results than classification without feature selection.