Goal-directed classification using linear machine decision trees
Draper, B.A.
Brodley, C.E.
Utgoff, P.E.
Dept. of Comput. Sci., Massachusetts Univ., Amherst, MA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Sep 1994
Volume: 16,
Issue: 9
On page(s): 888-893
ISSN: 0162-8828
References Cited: 11
CODEN: ITPIDJ
INSPEC Accession Number: 4774799
Digital Object Identifier: 10.1109/34.310684
Current Version Published: 2002-08-06
Abstract
Recent work in feature-based classification has focused on
nonparametric techniques that can classify instances even when the
underlying feature distributions are unknown. The inference algorithms
for training these techniques, however, are designed to maximize the
accuracy of the classifier, with all errors weighted equally. In many
applications, certain errors are far more costly than others, and the
need arises for nonparametric classification techniques that can be
trained to optimize task-specific cost functions. This correspondence
reviews the linear machine decision tree (LMDT) algorithm for inducing
multivariate decision trees, and shows how LMDT can be altered to induce
decision trees that minimize arbitrary misclassification cost functions
(MCF's). Demonstrations of pixel classification in outdoor scenes show
how MCF's can optimize the performance of embedded classifiers within
the context of larger image understanding systems
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