Quantifying Relevance of Input Features

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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.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning — IDEAL 2002 Third International Conference Manchester, UK, August 12–14, 2002 Proceedings
EditorsHujun Yin, Nigel Allinson, Richard Freeman, John Keane, Simon Hubbard
PublisherSpringer Berlin / Heidelberg
Pages685-695
Number of pages11
Volume2412
ISBN (Print)978-3-540-44025-3
DOIs
Publication statusPublished - 2002

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg

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