C-Net: a method for generating non-deterministic and dynamic multivariate decision trees
type: Item
instance of: scholarly article
title: C-Net: a method for generating non-deterministic and dynamic multivariate decision trees
zbMATH Open document ID: 0991.68627
DOI: 10.1007/PL00011665
author: R. Smith
published in: Knowledge and Information Systems
publication date: 2002-07-25T00:00:00Z
review text: 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, although usually 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.
Mathematics Subject Classification ID: 68U99
Mathematics Subject Classification ID: 68P15
Mathematics Subject Classification ID: 68P20
zbMATH DE Number: 1771464
zbMATH Keywords: C5
zbMATH Keywords: Multivariate decision trees
zbMATH Keywords: Neural networks
zbMATH Keywords: Univariate decision trees
MaRDI profile type: Publication
full work available at URL: pl00011665
OpenAlex ID: W2070970059
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description: scientific article; zbMATH DE number 1771464