An Improvement of One-Against-One Method for Multi-Class Support Vector Machine

Liu, Yang; Wang, Rui; Ying-sheng, Zeng

doi:10.1109/icmlc.2007.4370646

Cited by 17 publications

(6 citation statements)

References 6 publications

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“…In this paper, we propose an intra-classes correlation search, it would be interesting to study a different method of attributes regrouping to characterize a class versus all others, known as OAA (One-Against-All) or OVA (One-Vs-All) approaches [28,37]. Another attractive perspective consists in reducing the number of rules in SIF-INTRA by introducing rules' selection methods.…”

Section: Discussionmentioning

confidence: 99%

Attributes Regrouping in Fuzzy Rule Based Classification Systems: An Intra-Classes Approach

Borgi

Kalaï

Zgaya

2018

2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)

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Fuzzy rule-based classification systems (FRBCS) are able to build linguistic interpretable models, they automatically generate fuzzy if-then rules and use them to classify new observations. However, in these supervised learning systems, a high number of predictive attributes leads to an exponential increase of the number of generated rules. Moreover the antecedent conditions of the obtained rules are very large since they contain all the attributes that describe the examples. Therefore the accuracy of these systems as well as their interpretability degraded. To address this problem, we propose to use ensemble methods for FRBCS where the decisions of different classifiers are combined in order to form the final classification model. We are interested in particular in ensemble methods which split the attributes into subgroups and treat each subgroup separately. We propose to regroup attributes by correlation search among the training set elements that belongs to the same class, such an intra-classes correlation search allows to characterize each class separately. Several experiences were carried out on various data. The results show a reduction in the number of rules and of antecedents without altering accuracy, on the contrary classification rates are even improved.

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Section: Discussionmentioning

confidence: 99%

Attributes Regrouping in Fuzzy Rule Based Classification Systems: An Intra-Classes Approach

Borgi

Kalaï

Zgaya

2018

2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)

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“…In addressing the multi‐class classification problem, SVM can be used as one versus one classification or one versus all classification. The one versus one classification approach uses 15(6c 2 ) classifiers for a six‐class AFER problem, whereas one versus all approach uses a mere six classifiers for the same [34]. SVM is used as one versus one classifier and 5‐fold cross validation scheme is employed in all the experiments.…”

Section: Proposed Methodologymentioning

confidence: 99%

Automatic facial expression recognition combining texture and shape features from prominent facial regions

Kumar¹,

Kumar

et al. 2022

IET Image Processing

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Facial expression is one form of communication which being non‐verbal in nature precedes verbal communication in both origin and conception. Most of the existing methods for Automatic Facial Expression Recognition (AFER) are mainly focused on global feature extraction assuming that all facial regions contribute equal amount of discriminative information to predict the expression class. The detection and localization of facial regions that have significant contribution to expression recognition and extraction of highly discriminative feature distribution from those regions are not fully explored. The key contributions of the proposed work are developing novel feature distribution upon combining the discriminative power of shape and texture feature; determining the contribution of facial regions and identifying the prominent facial regions that hold abstract and highly discriminative information for expression recognition. The shape and texture features taken into consideration are Local Phase Quantization (LPQ), Local Binary Pattern (LBP), and Histogram of Oriented Gradients (HOG). Multiclass Support Vector Machine (MSVM) is used while one versus one classification. The proposed work is implemented on CK+, KDEF, and JAFFE benchmark facial expression datasets. The recognition rate of the proposed work is 94.2% on CK+ and 93.7% on KDEF, which is significantly more than the existing handcrafted feature‐based methods.

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“…Many researchers have proposed ways of dealing with the unbalancing of OVA approaches to compensate for the use of only O(N c ) binary classifiers [32]- [35]. Still, none of them represents the final answer on the matter because many ECOCbased solutions still require fewer for similar, or even better, classification results.…”

Section: State-of-the-artmentioning

confidence: 97%

Multiclass From Binary: Expanding One-Versus-All, One-Versus-One and ECOC-Based Approaches

Rocha

Goldenstein

2014

IEEE Trans. Neural Netw. Learning Syst.

161

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Recently, there has been a lot of success in the development of effective binary classifiers. Although many statistical classification techniques have natural multiclass extensions, some, such as the support vector machines, do not. The existing techniques for mapping multiclass problems onto a set of simpler binary classification problems run into serious efficiency problems when there are hundreds or even thousands of classes, and these are the scenarios where this paper's contributions shine. We introduce the concept of correlation and joint probability of base binary learners. We learn these properties during the training stage, group the binary leaner's based on their independence and, with a Bayesian approach, combine the results to predict the class of a new instance. Finally, we also discuss two additional strategies: one to reduce the number of required base learners in the multiclass classification, and another to find new base learners that might best complement the existing set. We use these two new procedures iteratively to complement the initial solution and improve the overall performance. This paper has two goals: finding the most discriminative binary classifiers to solve a multiclass problem and keeping up the efficiency, i.e., small number of base learners. We validate and compare the method with a diverse set of methods of the literature in several public available datasets that range from small (10 to 26 classes) to large multiclass problems (1000 classes) always using simple reproducible scenarios.

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An Improvement of One-Against-One Method for Multi-Class Support Vector Machine

Cited by 17 publications

References 6 publications

Attributes Regrouping in Fuzzy Rule Based Classification Systems: An Intra-Classes Approach

Attributes Regrouping in Fuzzy Rule Based Classification Systems: An Intra-Classes Approach

Automatic facial expression recognition combining texture and shape features from prominent facial regions

Multiclass From Binary: Expanding One-Versus-All, One-Versus-One and ECOC-Based Approaches

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