Hodge, Victoria Jane orcid.org/0000-0002-2469-0224, O'Keefe, Simon orcid.org/0000-0001-5957-2474 and Austin, Jim orcid.org/0000-0001-5762-8614 (2004) A Binary Neural Decision Table Classifier. In: Proceedings Brain Inspired Cognitive Systems 2004 (BICS 2004), 29 Aug - 01 Sep 2004.
Abstract
In this paper, we introduce a neural network-based decision table algorithm. We focus on the implementation details of the decision table algorithm when it is constructed using the neural network. Decision tables are simple supervised classifiers which, Kohavi demonstrated, can outperform state-of-the-art classifiers such as C4.5. We couple this power with the efficiency and flexibility of a binary associative-memory neural network. We demonstrate how the binary associative-memory neural network can form the decision table index to map between attribute values and data records. We also show how two attribute selection algorithms, which may be used to pre-select the attributes for the decision table, can easily be implemented within the binary associative-memory neural framework. The first attribute selector uses mutual information between attributes and classes to select the attributes that classify best. The second attribute selector uses a probabilistic approach to evaluate randomly selected attribute subsets.
Metadata
Item Type: | Conference or Workshop Item |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 21 Jun 2016 13:24 |
Last Modified: | 16 Oct 2024 20:36 |
Status: | Published |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:89520 |
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