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...

The enormous increase of the size in databases makes finding an optimal subset of features extremely difficult. In this paper, a new feature selection method is proposed that will allow any subset evaluator -including the wrapper evaluation methodto be used to find a group of features that will allow a distinction to be made between the different possible classes. The method, BARS (Best Agglome...

This technical report presents feature subset selection methods for two boosting classification frameworks: Adaboost and ADTboost.

The analysis of large biological data sets that arise in gene expression or proteomics experiments often involves the selection of a subset of the available features that supports efficient classification. Finding multiple, distinct solutions to the feature subset selection problem may lead to increased biological insights. In this paper we address the problem of finding multiple solutions to t...

This paper describes several known and some new methods for feature subset selection on large text data. Experimental comparison given on real-world data collected from Web users shows that characteristics of the problem domain and machine learning algorithm should be considered when feature scoring measure is selected. Our problem domain consists of hyperlinks given in a form of small-document...

Practical pattern classi cation and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classi ed. This paper presents an approach to the multi-criteria optimization problem of feature subset selection using a genetic algorithm. Our experiments demonstrate the feasibility of this approach for feature subse...

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...