On piecewise-linear classification
Herman, G.T.
Yeung, K.T.D.
Dept. of Radiol., Pennsylvania Univ., Philadelphia, PA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jul 1992
Volume: 14,
Issue: 7
On page(s): 782-786
ISSN: 0162-8828
References Cited: 10
CODEN: ITPIDJ
INSPEC Accession Number: 4241512
Digital Object Identifier: 10.1109/34.142914
Current Version Published: 2002-08-06
Abstract
The authors make use of a real data set containing 9-D
measurements of fine needle aspirates of a patient's breast for the
purpose of classifying a tumor's malignancy for which early stopping in
the generation of the separating hyperplanes is not appropriate. They
compare a piecewise-linear classification method with classification
based on a single linear separator. A precise methodology for comparing
the relative efficacy of two classification methods for a particular
task is described and is applied to the comparison on the breast cancer
data of the relative performances of the two versions of the
piecewise-linear classifier and the classification based on an optimal
linear separator. It is found that for this data set, the
piecewise-linear classifier that uses all the hyperplanes needed to
separate the training set outperforms the other two methods and that
these differences in performance are significant at the 0.001 level.
There is no statistically significant difference between the performance
of the other two methods. The authors discuss the relevance of these
results for this and other applications
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