We propose Very Simple Classifier (VSC) a novel method designed to incorporate the concepts of subsampling and locality in the definition of features to be used as the input of a perceptron. The rationale is that locality theoretically guarantees a bound on the generalization error. Each feature in VSC is a max-margin classifier built on randomly-selected pairs of samples. The locality in VSC is achieved by multiplying the value of the feature by a confidence measure that can be characterized in terms of the Chebichev inequality. The output of the layer is then fed in a output layer of neurons. The weights of the output layer are then determined by a regularized pseudoinverse. Extensive comparison of VSC against 9 competitors in the task of binary classification is carried out. Results on 22 benchmark datasets with fixed parameters show that VSC is competitive with the Multi Layer Perceptron (MLP) and outperforms the other competitors. An exploration of the parameter space shows VSC can outperform MLP.

Very simple classifier: A concept binary classifier to investigate features based on subsampling and locality / Masera, L.; Blanzieri, E.. - (2019), pp. 263-268. (Intervento presentato al convegno ESANN 2019 tenutosi a Bruges nel 24th-26th April 2019).

Very simple classifier: A concept binary classifier to investigate features based on subsampling and locality

Masera L.;Blanzieri E.
2019-01-01

Abstract

We propose Very Simple Classifier (VSC) a novel method designed to incorporate the concepts of subsampling and locality in the definition of features to be used as the input of a perceptron. The rationale is that locality theoretically guarantees a bound on the generalization error. Each feature in VSC is a max-margin classifier built on randomly-selected pairs of samples. The locality in VSC is achieved by multiplying the value of the feature by a confidence measure that can be characterized in terms of the Chebichev inequality. The output of the layer is then fed in a output layer of neurons. The weights of the output layer are then determined by a regularized pseudoinverse. Extensive comparison of VSC against 9 competitors in the task of binary classification is carried out. Results on 22 benchmark datasets with fixed parameters show that VSC is competitive with the Multi Layer Perceptron (MLP) and outperforms the other competitors. An exploration of the parameter space shows VSC can outperform MLP.
2019
ESANN 2019 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Louvain-La-Neuve
i6doc.com
978-287-587-065-0
Masera, L.; Blanzieri, E.
Very simple classifier: A concept binary classifier to investigate features based on subsampling and locality / Masera, L.; Blanzieri, E.. - (2019), pp. 263-268. (Intervento presentato al convegno ESANN 2019 tenutosi a Bruges nel 24th-26th April 2019).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/251968
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