Many feature subset selection algorithms have been proposed and discussed for years. However, the problem of finding the optimal feature subset from full data still remains to be a difficult problem. In this paper, we propose novel methods to find the relevant feature subset by using biologically-inspired algorithms such as Genetic Algorithm and Particle Swarm Optimization. We also propose a va...

Image feature selection (FS) is an important task which can affect the performance of image classification and recognition. In this paper, we present a feature selection algorithm based on ant colony optimization (ACO). For n features, most ACO-based feature selection methods use a complete graph with O(n) edges. However, the artificial ants in the proposed algorithm traverse on a digraph with ...

The computational complexity of a texture classification algorithm is limited by the dimensionality of the feature space. Although finding the optimal feature subset is a NP-hard problem [1], a feature selection algorithm that can reduce the dimensionality of problem is often desirable. In this paper, we report work on a feature selection algorithm for texture classification using two subband f...

Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has been investigated by many researchers. In this paper, a novel feature subset search procedure that utilizes...

The Ant Colony Optimization (ACO) is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that ut...

In this paper an axiomatic characterisation of feature subset selection is presented. Two axioms are presented: suuciency axiom | preservation of learning information, and necessity axiom | minimising encoding length. The suuciency axiom concerns the existing dataset and is derived based on the following understanding: any selected feature subset should be able to describe the training dataset ...

Article history: Received 30 January 2009 Received in revised form 31 May 2009 Accepted 17 June 2009

Past work on object detection has emphasized the issues of feature extraction and classification, however, relatively less attention has been given to the critical issue of feature selection. The main trend in feature extraction has been representing the data in a lower dimensional space, for example, using Principal Component Analysis (PCA). Without using an effective scheme to select an appro...

Subset Feature Selection problems can have several attributes which may make Messy Ge netic Algorithms an appropriate optimization method First competitive solutions may of ten use only a small percentage of the total available features this can not only o er an advantage to Messy Genetic Algorithms it may also cause problems for other types of evolutionary algorithms Second the evalu ation of ...

This paper proposes a feature selection technique for software clustering which can be used in the architecture recovery of software systems. The recovered architecture can then be used in the subsequent phases of software maintenance, reuse and re-engineering. A number of diverse features could be extracted from the source code of software systems, however, some of the extracted features may h...