Quantifying Relevance of Input Features

Wang, W. (2002) Quantifying Relevance of Input Features. In: Intelligent Data Engineering and Automated Learning — IDEAL 2002 Third International Conference Manchester, UK, August 12–14, 2002 Proceedings. Lecture Notes in Computer Science, 2412 . Springer Berlin / Heidelberg, pp. 685-695. ISBN 978-3-540-44025-3

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Abstract

Identifying and quantifying relevance of input features are particularly useful in data mining when dealing with ill-understood real-world data defined problems. The conventional methods, such as statistics and correlation analysis, appear to be less effective because the data of such type of problems usually contains high-level noise and the actual distributions of attributes are unknown. This papers presents a neural-network based method to identify relevant input features and quantify their general and specified relevance. An application to a real-world problem, i.e. osteoporosis prediction, demonstrates that the method is able to quantify the impacts of risk factors, and then select the most salient ones to train neural networks for improving prediction accuracy.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences

University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
Related URLs:
Depositing User: Vishal Gautam
Date Deposited: 04 Jul 2011 09:01
Last Modified: 22 Apr 2020 10:25
URI: https://ueaeprints.uea.ac.uk/id/eprint/22741
DOI: 10.1007/3-540-45675-9_89

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