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Probability estimation for multiclass problems combining SVMs and neural networks

Probability estimation for multiclass problems combining SVMs and neural networks
Probability estimation for multiclass problems combining SVMs and neural networks
This paper addresses the problem of probability estimation in Multiclass classification tasks combining two well-known data mining techniques: Support Vector Machines and Neural Networks. We present an algorithm which uses both techniques in a two-step procedure. The first step employs Support Vector Machines within a One-vs-All reduction from multiclass to binary approach to obtain the distances between each observation and the Support Vectors representing the classes. The second step uses these distances as inputs for a Neural Network, built with an entropy cost function and softmax transfer function for the output layer where class membership is used for training. Consequently, this network estimates probabilities of class membership for new observations. A benchmark using different databases demonstrates that the proposed algorithm is highly competitive with the most recent techniques for multiclass probability estimation
475-489
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
L'Huillier, G.
8a14f86f-e86c-4c03-9f12-56ddd2999c5e
Luis Lobato, J.
58fc37d6-ec65-4dc0-8832-ab81ddb24a76
Weber, R.
d60b7a82-1576-464b-81b5-86d74b0574f5
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
L'Huillier, G.
8a14f86f-e86c-4c03-9f12-56ddd2999c5e
Luis Lobato, J.
58fc37d6-ec65-4dc0-8832-ab81ddb24a76
Weber, R.
d60b7a82-1576-464b-81b5-86d74b0574f5

Bravo, Cristian, L'Huillier, G., Luis Lobato, J. and Weber, R. (2010) Probability estimation for multiclass problems combining SVMs and neural networks. Neural Network World, 4, 475-489.

Record type: Article

Abstract

This paper addresses the problem of probability estimation in Multiclass classification tasks combining two well-known data mining techniques: Support Vector Machines and Neural Networks. We present an algorithm which uses both techniques in a two-step procedure. The first step employs Support Vector Machines within a One-vs-All reduction from multiclass to binary approach to obtain the distances between each observation and the Support Vectors representing the classes. The second step uses these distances as inputs for a Neural Network, built with an entropy cost function and softmax transfer function for the output layer where class membership is used for training. Consequently, this network estimates probabilities of class membership for new observations. A benchmark using different databases demonstrates that the proposed algorithm is highly competitive with the most recent techniques for multiclass probability estimation

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More information

Published date: 2010
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 396675
URI: http://eprints.soton.ac.uk/id/eprint/396675
PURE UUID: 5e36ebdb-1f56-401b-bd13-55796d5cc315
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

Catalogue record

Date deposited: 10 Jun 2016 09:03
Last modified: 20 Jul 2019 00:48

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