| Record |
Author  |
Miguel Angel Bautista; Xavier Baro; Oriol Pujol; Petia Radeva; Jordi Vitria; Sergio Escalera |
| Title |
Compact Evolutive Design of Error-Correcting Output Codes |
Type |
Conference Article |
| Year |
2010 |
Publication |
Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
119-128 |
| Keywords |
Ensemble of Dichotomizers; Error-Correcting Output Codes; Evolutionary optimization |
| Abstract |
The classi cation of large number of object categories is a challenging trend in the Machine Learning eld. In literature, this is often addressed using an ensemble of classi ers. In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for the combination of classi ers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classi ers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classi ers. Evolutionary computation is used for tuning the parameters of the classi ers and looking for the best Minimal ECOC code con guration. The results over several public UCI data sets and a challenging multi-class Computer Vision problem show that the proposed methodology obtains comparable and even better results than state-of-the-art ECOC methodologies with far less number of dichotomizers. |
| Address |
Barcelona (Spain) |
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Expedition |
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Conference |
SUEMA |
| Notes |
OR;MILAB;HUPBA;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ BBP2010 |
Serial |
1363 |
| Permanent link to this record |
