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Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
On the Decoding Process in Ternary Error-Correcting Output Codes |
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Journal Article |
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Year |
2010 |
Publication |
IEEE on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
32 |
Issue |
1 |
Pages |
120–134 |
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Abstract |
A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-correcting output codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a ldquodo not carerdquo symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI machine learning repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved. |
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0162-8828 |
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MILAB;HUPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2010b |
Serial |
1277 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Error-Correcting Output Codes Library |
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Journal Article |
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Year |
2010 |
Publication |
Journal of Machine Learning Research |
Abbreviated Journal |
JMLR |
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11 |
Issue |
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Pages |
661-664 |
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Abstract |
(Feb):661−664
In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier. |
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1532-4435 |
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MILAB;HUPBA |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2010c |
Serial |
1286 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Re-coding ECOCs without retraining |
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Journal Article |
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2010 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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31 |
Issue |
7 |
Pages |
555–562 |
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Abstract |
A standard way to deal with multi-class categorization problems is by the combination of binary classifiers in a pairwise voting procedure. Recently, this classical approach has been formalized in the Error-Correcting Output Codes (ECOC) framework. In the ECOC framework, the one-versus-one coding demonstrates to achieve higher performance than the rest of coding designs. The binary problems that we train in the one-versus-one strategy are significantly smaller than in the rest of designs, and usually easier to be learnt, taking into account the smaller overlapping between classes. However, a high percentage of the positions coded by zero of the coding matrix, which implies a high sparseness degree, does not codify meta-class membership information. In this paper, we show that using the training data we can redefine without re-training, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information helps the system to increase its generalization capability. Moreover, the new re-coding strategy is generalized to be applied over any binary code. The results over several UCI Machine Learning repository data sets and two real multi-class problems show that performance improvements can be obtained re-coding the classical one-versus-one and Sparse random designs compared to different state-of-the-art ECOC configurations. |
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Elsevier |
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Notes |
MILAB;HUPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2010e |
Serial |
1338 |
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Author |
Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes |
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Journal Article |
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Year |
2009 |
Publication |
Journal of Signal Processing Systems |
Abbreviated Journal |
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55 |
Issue |
1-3 |
Pages |
35–47 |
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Abstract |
Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches. |
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ISSN |
1939-8018 |
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Notes |
MILAB;HuPBA |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EPM2009 |
Serial |
1258 |
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Author |
Sergio Escalera; Jordi Gonzalez; Xavier Baro; Jamie Shotton |
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Title |
Guest Editor Introduction to the Special Issue on Multimodal Human Pose Recovery and Behavior Analysis |
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Journal Article |
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Year |
2016 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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Volume |
28 |
Issue |
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Pages |
1489 - 1491 |
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The sixteen papers in this special section focus on human pose recovery and behavior analysis (HuPBA). This is one of the most challenging topics in computer vision, pattern analysis, and machine learning. It is of critical importance for application areas that include gaming, computer interaction, human robot interaction, security, commerce, assistive technologies and rehabilitation, sports, sign language recognition, and driver assistance technology, to mention just a few. In essence, HuPBA requires dealing with the articulated nature of the human body, changes in appearance due to clothing, and the inherent problems of clutter scenes, such as background artifacts, occlusions, and illumination changes. These papers represent the most recent research in this field, including new methods considering still images, image sequences, depth data, stereo vision, 3D vision, audio, and IMUs, among others. |
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HuPBA; ISE;MV; |
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no |
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Call Number |
Admin @ si @ |
Serial |
2851 |
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