Constrained classifier: a novel approach to nonlinear classification
Simple classifiers have the advantage of more generalization capability with the side effect of less power. It would be a good idea if we could build a classifier which is as simple as possible while giving it the ability of classifying complex patterns. In this paper, a hybrid classifier called constrained classifier is presented that classifies most of the input patterns using a simple, for example, a linear classifier. It performs the classification in four steps. In the Dividing step, the input patterns are divided into linearly separable and nonlinearly separable groups. The patterns belonging to the first group are classified using a simple classifier while the second group patterns (named constraints) are modeled in the Modeling step. The results of previous steps are merged together in the Combining step. The Evaluation step tests and fine tunes the membership of patterns into two groups. The experimental results of comparison of the new classifier with famous classifiers such as support vector machine, k-NN, and Classification and Regression Trees are very encouraging.
linear classification multiple classifier system constrained classification classifier boosting