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.
|Published - 29 Aug 2004
|Proceedings Brain Inspired Cognitive Systems 2004 (BICS 2004) - Stirling, United Kingdom
Duration: 29 Aug 2004 → 1 Sept 2004
|Proceedings Brain Inspired Cognitive Systems 2004 (BICS 2004)
|29/08/04 → 1/09/04