C-Net: a method for generating non-deterministic and dynamic multivariate decision trees
Abbass, Hussein A., Towsey, Michael W., & Finn, Gerard D. (2001) C-Net: a method for generating non-deterministic and dynamic multivariate decision trees. Knowledge and Information Systems, 3(2), pp. 184-197.
Despite the fact that artificial neural networks (ANNs) are universal function approximators, their black box nature (that is, their lack of direct interpretability or expressive power) limits their utility. In contrast, univariate decision trees (UDTs) have expressive power, usually though they are not as accurate as ANNs. We propose an improvement, C-Net, for both the expressiveness of ANNs and the accuracy of UDTs by consolidating both technologies for generating multivariate decision trees (MDTs). In addition, we introduce a new concept, recurrent decision trees, where C-Net uses recurrent neural networks to generate an MDT with a recurrent feature. That is, a memory is associated with each node in the tree with a recursive condition which replaces the conventional linear one. Furthermore, we show empirically that, in our test cases, our proposed method achieves a balance of comprehensibility and accuracy intermediate between ANNs and UDTs. MDTs are found to be intermediate since they are more expressive than ANNs and, more accurate than UDTs. Moreover, in all cases MDTs are more compact (i.e. smaller tree size) than UDTs.
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|Item Type:||Journal Article|
|Keywords:||multivariate decision trees, neural networks, univariate decision trees|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2001 Springer|
|Copyright Statement:||The original publication is available at SpringerLink
|Deposited On:||11 May 2007 00:00|
|Last Modified:||09 Jun 2010 12:40|
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