Feature selection (FS) is a major issue in developing efficient pattern recognition systems. FS refers to the selection of the most appropriate subset of features that describes (adequately) a given classification task. The objective of this paper is to perform a thorough analysis of the performance and efficiency of feature selection algorithms (FSAs). The analysis covers a variety of importan...

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In this paper, we present a set of wavelet moment invariants, together with a discriminative feature selection method, for the classification of seemingly similar objects with subtle differences. These invariant features are selected automatically based on the discrimination measures defined for the invariant features. Using a minimum-distance classifier, our wavelet moment invariants achieved ...

Considering the classification problem in which class priors or misallocation costs are not known precisely, receiver operator characteristic (ROC) analysis has become a standard tool in pattern recognition for obtaining integrated performance measures to cope with the uncertainty. Similarly, in situations in which priors may vary in application, the ROC can be used to inspect performance over ...

A method based on Electrical Capacitance Tomography (ECT) and an improved Least Squares Support Vector Machine (LS-SVM) is proposed for void fraction measurement of oil-gas two-phase flow. In the modeling stage, to solve the two problems in LS-SVM, pruning skills are employed to make LS-SVM sparse and robust; then the Real-Coded Genetic Algorithm is introduced to solve the difficult problem...

Adaptation Aspect of an organism that was created by the process of natural selection because it served an adaptive function Adaptive Contributing to the eventual reproduction of an organism or its relatives Bayes's theorem Specifies the probability that a hypothesis is true, given new data; P(HJD) = P(H)P(DIH)/P(D), where H is the hypothesis and D is the new data Cognitive psychology Study of ...

Dynamic time warping (DTW) has been widely used in various pattern recognition and time series data mining applications. However, as examples will illustrate, both the classic DTW and its later alternative, derivative DTW, may fail to align a pair of sequences on their common trends or patterns. Furthermore, the learning capability of any supervised learning algorithm based on classic/derivativ...

Linear separability of data sets is one of the basic concepts in the theory of neural networks and pattern recognition. Data sets are often linearly separable because of their high dimensionality. Such is the case of genomic data, in which a small number of cases is represented in a space with extremely high dimensionality. An evaluation of linear separability of two data sets can be combined w...

Selecting a set offeatures which is optimalfor a given task is the problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The concept of reduction of the decision table based on the rough set is very useful for feature selection. In this paper, a genetic algorithm based approach is presented to search the rel...