Piecewise linear classifiers using binary tree structure and genetic algorithm

Abstract 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.