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Piecewise linear classifiers using binary tree structure and genetic algorithm
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DOI：10.1016/0031-3203(96)00019-2.png)
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Abstract
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A linear decision binary tree structure is proposed in constructing piecewise linear classifiers with the Genetic Algorithm (GA) being shaped and employed at each nonterminal node in order to search for a linear decision function, optimal in the sense of maximum impurity reduction. The methodology works for both the two-class and multi-class cases. In comparison to several other well-known methods, the proposed Binary Tree-Genetic Algorithm (BTGA) is demonstrated to produce a much lower cross validation misclassification rate. Finally, a modified BTGA is applied to the important pap smear cell classification. This results in a spectrum for the combination of the highest desirable sensitivity along with the lowest possible false alarm rate ranging from 27.34% sensitivity, 0.62% false alarm rate to 97.02% sensitivity, 50.24% false alarm rate from resubstitution validation. The multiple choices offered by the spectrum for the sensitivity-false alarm rate combination will provide the flexibility needed for the pap smear slide classification. Copyright (C) 1996 Pattern Recognition Society.
Keywords:
piecewise linear
binary tree
impurity
genetic algorithm
cell classification
sensitivity
false alarm rate
misclassification rate
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