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An evolutionary-based approach to learning multiple decision models from underrepresented data

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

The use of multiple Decision Models (DMs) enables to enhance the accuracy in decisions and at the same time allows users to evaluate the confidence in decision making. In this paper we explore the ability of multiple DMs to learn from a small amount of verified data. This becomes important when data samples are difficult to collect and verify. We propose an evolutionary-based approach to solving this problem. The proposed technique is examined on a few clinical problems presented by a small amount of data.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 07/11/2008

Publication status

Published - 07/11/2008

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9780769533049

ISBN (Electronic)

9780769533049

External Publication IDs

  • handle.net: 10547/270597
  • Scopus: 57649178583

Host publication title

nan

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