This study introduces a novel feature selection approach CMICOT, which is a further evolution of filter methods with sequential forward selection (SFS) whose scoring functions are based on conditional mutual information (MI). We state and study a novel saddle point (max-min) optimization problem to build a scoring function that is able to identify joint interactions between several features. Th...

This paper proposes a feature selection method that combines various feature selection techniques. Feature selection has been realized as one of the most important processes in various applications, especially pattern classification problems. When too many attributes are involved, training a machine to classify patterns into their respective classes is seemingly impossible. Hence, selecting goo...

In this paper, we present a novel approach for the classification of zoom-endoscopy images based on the pit-pattern classification scheme. Our feature generation step is based on the computation of a set of statistical features in the wavelet-domain. In the classification step, we employ a one-against-one approach using 1-Nearest Neighbor classifiers together with sequential forward feature sel...

A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimation of the classification rate. The estimation is done using the k-fold cross validation. In order to perform fast in terms of computing tim...

This paper presents grammatical evolution (GE) as an approach to select and combine features for detecting epileptic oscillations within clinical intracranial electroencephalogram (iEEG) recordings of patients with epilepsy. Clinical iEEG is used in preoperative evaluations of a patient who may have surgery to treat epileptic seizures. Literature suggests that pathological oscillations may indi...

We address the feature subset selection problem for classification tasks. We examine the performance of two hybrid strategies that directly search on a ranked list of features and compare them with two widely used algorithms, the fast correlation based filter (FCBF) and sequential forward selection (SFS). The proposed hybrid approaches provide the possibility of efficiently applying any subset ...

Feature selection methods are usually evaluated by wrapping specific classifiers and datasets in the evaluation process, resulting very often in unfair comparisons between methods. In this work, we develop a theoretical framework that allows obtaining the true feature ordering of two-dimensional sequential forward feature selection methods based on mutual information, which is independent of en...

In this paper we explore the use of feature selection techniques to improve the generalization performance of pattern recognizers for computer aided diagnosis CAD We apply a modi ed version of the sequential forward oating selection SFFS of Pudil et al to the problem of selecting an optimal feature subset for mass detection in digitized mammograms The complete feature set consists of multi scal...

Most of the widely used pattern classification algorithms, such as Support Vector Machines (SVM), are sensitive to the presence of irrelevant or redundant features in the training data. Automatic feature selection algorithms aim at selecting a subset of features present in a given dataset so that the achieved accuracy of the following classifier can be maximized. Feature selection algorithms ar...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...