Finding an optimal set of classification exemplars (OSCE) by using integer linear programming

This paper describes how to classify a data set by using an optimum set of exemplar to determine the label of an instance among a set of data for solving classification run time problem in large data set. The goal of this paper is to find a way to speed up the classification run time by choosing a set of exemplars. We used linear programming problems to optimize a hinge loss cost function, in which estimated label and actual label is used to train the classification. Estimated label is calculated by measuring Euclidean distance of a query point to all of its nearest neighbors which is multiplied by some weights and an actual label value. To select some exemplars with none zero weights. Two solution is suggested to have a better result. One of them is choosing smaller neighborhood or k closer neighbors. The other one is using LP and thresholding to select some maximum of achieved unknown variable which are more significant in finding a set of exemplar. Also, there is trade off between run time classifier and accuracy. In large data set, OSCE classifier has better performance than ANN and K-NN cluster

کلیدواژه ها:

Integer linear programming (ILP) ، linear pro- gramming (LP) ، exemplar ، hinge loss function