Benefit Maximization in Classification on Feature Projections

H.A. Güvenir (Turkey)


Machine learning, feature projection, voting, benefit maximization


In some domains, the cost of a wrong classification may be different for all pairs of predicted and actual classes. Also the benefit of a correct prediction is different for each class. In this paper, a new classification algorithm, called BCFP (for Benefit Maximizing Classifier on Feature Projections), is presented. The BCFP classifier learns a set of classification rules that will predict the class of a new instance with maximum benefit or minimum cost. BCFP represents a concept in the form of feature projections on each feature dimension separately. Classification in the BCFP algorithm is based on a voting among the individual predictions made on each feature. A genetic algorithm is used to select the relevant features. The performance of the BCFP algorithm is evaluated in terms of accuracy. As a case study, the BCFP algorithm is applied to the problem of diagnosis of gastric carcinoma. A lesion can be an indicator of one of nine different levels of gastric carcinoma. The benefit of correct classification of early levels is much more than that of late cases. Also, the cost of wrong classifications is different for all classes.

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