The University of Southampton
University of Southampton Institutional Repository

A hybrid system for probability estimation in multiclass problems combining SVMs and neural networks

A hybrid system for probability estimation in multiclass problems combining SVMs and neural networks
A hybrid system for probability estimation in 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
649-654
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Lobato, Jose Luis
70679249-6a4f-4cfa-b3fd-6d53b3fc304f
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
L'Huillier, Gaston
2654ebc3-30c3-43ae-8696-7e2268306bb4
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Lobato, Jose Luis
70679249-6a4f-4cfa-b3fd-6d53b3fc304f
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
L'Huillier, Gaston
2654ebc3-30c3-43ae-8696-7e2268306bb4

Bravo, Cristian, Lobato, Jose Luis, Weber, Richard and L'Huillier, Gaston (2008) A hybrid system for probability estimation in multiclass problems combining SVMs and neural networks. Eighth International Conference on Hybrid Intelligent Systems (HIS '08), Spain. 10 - 12 Sep 2008. pp. 649-654 . (doi:10.1109/HIS.2008.112).

Record type: Conference or Workshop Item (Other)

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

Full text not available from this repository.

More information

Published date: 2008
Venue - Dates: Eighth International Conference on Hybrid Intelligent Systems (HIS '08), Spain, 2008-09-10 - 2008-09-12
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 396685
URI: https://eprints.soton.ac.uk/id/eprint/396685
PURE UUID: d62aa323-ef3c-43ba-840c-b53bfb0af358
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

Catalogue record

Date deposited: 10 Jun 2016 10:56
Last modified: 06 Jun 2018 12:35

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×