A binary neural decision table classifier

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. Initially, we demonstrate how the binary associative-memory neural network can form the decision table index to map between attribute values and data records and subsequently we show how two attribute selection algorithms can be used to pre-select attributes for this decision table. The attribute selection algorithms are easily implemented within the same binary associative-memory framework producing a tightly coupled, two-tier system allowing attribute selection and decision table indexing. 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.