Identification of Feature-Salience

Wang, W., Jones, P. and Partridge, D. (2000) Identification of Feature-Salience. In: The IEEE-INNS-ENNS, International Joint Conference on Neural Networks (IJCNN 2000), 2000-07-24 - 2000-07-27.

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Abstract

In this paper we present two techniques designed to identify the relative salience of features in a data-defined problem with respect to their ability to predict a category outcome-e.g., which features of a character contribute most to accurate prediction of outcome. The first technique we proposed is a neural-net based clamping technique and another is based on inductive learning algorithm-decision tree's heuristic. They are compared with a number of other techniques, i.e., automatic relevance determination (ARD), weight-product, random selection, in addition to a standard statistical technique-linear correlation analysis. 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 as well as the effectiveness with which high-salience subsets of features can be identified in the context of ill-understood and potentially noisy real-world data

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 21 Jun 2011 18:26
Last Modified: 26 Oct 2018 10:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/22551
DOI: 10.1109/IJCNN.2000.861528

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