Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

July 1, 2001, Vol. 13, No. 7, Pages 1603-1623
(doi: 10.1162/089976601750265027)
© 2001 Massachusetts Institute of Technology
A Comparative Study of Feature-Salience Ranking Techniques
Article PDF (1.46 MB)
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.