Can I read Constrained Cascade Generalization of Decision Trees on EtoBox?

Constrained Cascade Generalization of Decision Trees by H. Zhao; Sudha Ram is a Computer Science article available to read on EtoBox.

What is Constrained Cascade Generalization of Decision Trees about?

While decision tree techniques have been widely used in classification applications, a shortcoming of many decision tree inducers is that they do not learn intermediate concepts, i.e., at each node, only one of the original features is involved in the branching decision. Combining other classification methods, which learn intermediate concepts, with decision tree inducers can produce more flexible decision boundaries that separate different classes, potentially improving classification accuracy. We propose a generic algorithm for cascade generalization of decision tree inducers with the maximum cascading depth as a parameter to constrain the degree of cascading. Cascading methods proposed in the past, i.e., loose coupling and tight coupling, are strictly special cases of this new algorithm. We have empirically evaluated the proposed algorithm using logistic regression and C4.5 as base inducers on 32 UCI data sets and found that neither loose coupling nor tight coupling is always the best cascading strategy and that the maximum cascading depth in the proposed algorithm can be tuned for better classification accuracy. We have also empirically compared the proposed algorithm and ensem

Who reads Constrained Cascade Generalization of Decision Trees?

Author

H. Zhao; Sudha Ram

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2004

Language

EN

Field

Computer Science (Physical Sciences)