Please use this identifier to cite or link to this item: https://doi.org/10.1109/3477.764880
Title: A connectionist approach to generating oblique decision trees
Authors: Setiono, R. 
Liu, H. 
Keywords: Decision trees
Network pruning
Neural networks
Oblique decision rules
Issue Date: 1999
Source: Setiono, R., Liu, H. (1999). A connectionist approach to generating oblique decision trees. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 29 (3) : 440-444. ScholarBank@NUS Repository. https://doi.org/10.1109/3477.764880
Abstract: Neural networks and decision tree methods are two common approaches to pattern classification. While neural networks can achieve high predictive accuracy rates, the decision boundaries they form are highly nonlinear and generally difficult to comprehend. Decision trees, on the other hand, can be readily translated into a set of rules. In this paper, we present a novel algorithm for generating oblique decision trees that capitalizes on the strength of both approaches. Oblique decision trees classify the patterns by testing on linear combinations of the input attributes. As a result, an oblique decision tree is usually much smaller than the univariate tree generated for the same domain. Our algorithm consists of two components: connectionist and symbolic. A three-layer feedforward neural network is constructed and pruned, a decision tree is then built from the hidden unit activation values of the pruned network. An oblique decision tree is obtained by expressing the activation values using the original input attributes. We test our algorithm on a wide range of problems. The oblique decision trees generated by the algorithm preserve the high accuracy of the neural networks, while keeping the explicitness of decision trees. Moreover, they outperform univariate decision trees generated by the symbolic approach and oblique decision trees built by other approaches in accuracy and tree size. © 1999 IEEE.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/42903
ISSN: 10834419
DOI: 10.1109/3477.764880
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