Please use this identifier to cite or link to this item: https://doi.org/10.1109/3477.764880
DC FieldValue
dc.titleA connectionist approach to generating oblique decision trees
dc.contributor.authorSetiono, R.
dc.contributor.authorLiu, H.
dc.date.accessioned2013-07-15T05:25:25Z
dc.date.available2013-07-15T05:25:25Z
dc.date.issued1999
dc.identifier.citationSetiono, 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
dc.identifier.issn10834419
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42903
dc.description.abstractNeural 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/3477.764880
dc.sourceScopus
dc.subjectDecision trees
dc.subjectNetwork pruning
dc.subjectNeural networks
dc.subjectOblique decision rules
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.departmentINFORMATION SYSTEMS
dc.description.doi10.1109/3477.764880
dc.description.sourcetitleIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
dc.description.volume29
dc.description.issue3
dc.description.page440-444
dc.description.codenITSCF
dc.identifier.isiut000080371500011
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