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We propose Very Simple Classifier (VSC) a novel method designed toincorporate the concepts of subsampling and locality in the definition offeatures to be used as the input of a perceptron. The rationale is thatlocality theoretically guarantees a bound on the generalization error. Eachfeature in VSC is a max-margin classifier built on randomly-selected pairs ofsamples. The locality in VSC is achieved by multiplying the value of thefeature by a confidence measure that can be characterized in terms of theChebichev inequality. The output of the layer is then fed in a output layer ofneurons. The weights of the output layer are then determined by a regularizedpseudoinverse. Extensive comparison of VSC against 9 competitors in the task ofbinary classification is carried out. Results on 22 benchmark datasets withfixed parameters show that VSC is competitive with the Multi Layer Perceptron(MLP) and outperforms the other competitors. An exploration of the parameterspace shows VSC can outperform MLP.

Very Simple Classifier: a Concept Binary Classifier toInvestigate Features Based on Subsampling and Localility