Exploiting diversity of margin-based classifiers

Enrique Romero, Xavier Carreras, Lluís Màrquez

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

An experimental comparison among Support Vector Machines, AdaBoost and a recently proposed model for maximizing the margin with Feed-forward Neural Networks has been made on a real-world classification problem, namely Text Categorization. The results obtained when comparing their agreement on the predictions show that similar performance does not imply similar predictions, suggesting that different models can be combined to obtain better performance. As a consequence of the study, we derived a very simple confidence measure of the prediction of the tested margin-based classifiers. This measure is based on the margin curve. The combination of margin-based classifiers with this confidence measure lead to a marked improvement on the performance of the system, when combined with several well-known combination schemes.

Original languageEnglish
Pages (from-to)419-424
Number of pages6
JournalIEEE International Conference on Neural Networks - Conference Proceedings
Volume1
Publication statusPublished - 1 Dec 2004
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 25 Jul 200429 Jul 2004

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ASJC Scopus subject areas

  • Software

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