Feature selection and instance selection are two important data preprocessing steps in data
mining, where the former is aimed at removing some irrelevant and/or redundant features …

This paper presents a novel approach for object detection using a feature construction
method called Evolution-COnstructed (ECO) features. Most other object recognition …

Feature selection is a key issue in pattern recognition, specially when prior knowledge of the
most discriminant features is not available. Moreover, in order to perform the classification …

Feature selection (FS) for classification tasks in machine learning and data mining has
attracted significant attention. Recently, increasing metaheuristic optimization algorithms …

Multi-objective genetic-clustering algorithms are based on optimization which optimizes
several objectives simultaneously. In multi-objective optimization problem (MOP), different …

In covering rough set theory, the basic problem is the calculation of lower and upper
approximations for subsets of a covering approximation space. For a covering …

Abstract As Granular Computing has gained interest, more research has lead into using
different representations for Information Granules, ie, rough sets, intervals, quotient space …

The most widely used speech representation is based on the mel-frequency cepstral
coefficients, which incorporates biologically inspired characteristics into artificial …

In this paper, we try to solve the feature ranking problem through an allocation of information
granularity. In many real applications, people are more concerned with an ordered …

Based on the notions of neighborhood and complementary neighborhood, we consider the
classification of coverings in the covering rough set theory. We present a classification rule …