A comparative study of feature-salience ranking techniques

Wang, W., Jones, P. and Partridge, D. (2001) A comparative study of feature-salience ranking techniques. Neural Computation, 13 (7). pp. 1603-1623. ISSN 0899-7667

Full text not available from this repository. (Request a copy)

Abstract

We assess the relative merits of a number of techniques designed to determine the relative salience of the elements of a feature set with respect to their ability to predict a category outcome-for example, which features of a character contribute most to accurate character recognition. A number of different neural-net-based techniques have been proposed (by us and others) in addition to a standard statistical technique, and we add a technique based on inductively generated decision trees. The salience of the features that compose a proposed set is an important problem to solve efficiently and effectively, not only for neural computing technology but also in order to provide a sound basis for any attempt to design an optimal computational system. The focus of this study is the efficiency and the effectiveness with which high-salience subsets of features can be identified in the context of ill-understood and potentially noisy real-world data. Our two simple approaches, weight clamping using a neural network and feature ranking using a decision tree, generally provide a good, consistent ordering of features. In addition, linear correlation often works well.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and Statistics
Depositing User:	Vishal Gautam
Date Deposited:	13 Jun 2011 13:49
Last Modified:	15 Dec 2022 01:45
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22549
DOI:	10.1162/089976601750265027

Actions (login required)

View Item