An iterative growing and pruning algorithm for classification treedesign
Gelfand, S.B.
Ravishankar, C.S.
Delp, E.J.
Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Feb 1991
Volume: 13,
Issue: 2
On page(s): 163-174
ISSN: 0162-8828
References Cited: 33
CODEN: ITPIDJ
INSPEC Accession Number: 3880506
Digital Object Identifier: 10.1109/34.67645
Current Version Published: 2002-08-06
Abstract
A critical issue in classification tree design-obtaining
right-sized trees, i.e. trees which neither underfit nor overfit the
data-is addressed. Instead of stopping rules to halt partitioning, the
approach of growing a large tree with pure terminal nodes and
selectively pruning it back is used. A new efficient iterative method is
proposed to grow and prune classification trees. This method divides the
data sample into two subsets and iteratively grows a tree with one
subset and prunes it with the other subset, successively interchanging
the roles of the two subsets. The convergence and other properties of
the algorithm are established. Theoretical and practical considerations
suggest that the iterative free growing and pruning algorithm should
perform better and require less computation than other widely used tree
growing and pruning algorithms. Numerical results on a waveform
recognition problem are presented to support this view
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