A subspace approach to error correcting output codes

Bagheri, Mohammad Ali; Montazer, Gholam Ali; Kabir, Ehsanollah

doi:10.1016/j.patrec.2012.09.010

Cited by 32 publications

(24 citation statements)

References 19 publications

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“…The most popular approach is the Error-Correcting Output Codes (ECOC) [19], which creates a unique code for each binary classifiers [62]. Many works regarding ECOC concentrate on the design of compact and reliable codewords to allow handling many classes [5]. Another popular approach from this group are Decision Templates [39], that create prototypes of support function values of each classifier for a given class.…”

Section: Binary Classifiers For Decomposing Multi-class Datasetsmentioning

confidence: 99%

On the usefulness of one-class classifier ensembles for decomposition of multi-class problems

Krawczyk

Woźniak

Herrera

2015

Pattern Recognition

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Section: Binary Classifiers For Decomposing Multi-class Datasetsmentioning

confidence: 99%

On the usefulness of one-class classifier ensembles for decomposition of multi-class problems

Krawczyk

Woźniak

Herrera

2015

Pattern Recognition

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“…For instance, ensembles with nine other weighting strategies are employed to conduct the 7-class facial emotion recognition including (non-weighted) majority voting, minimum and maximum probability, distribution summation, average of probabilities, product of probabilities, Bayesian combination, decision templates and Dempster-Shafer [65]. In this research, in order to make feasible comparison, all the above weighting methods are implemented with four NN base classifiers similar to those used for our proposed ensemble.…”

Section: Facial Emotion Recognition Using An Adaptive Ensemble Classimentioning

confidence: 99%

Intelligent facial emotion recognition using a layered encoding cascade optimization model

Neoh

Zhang

Mistry

et al. 2015

Applied Soft Computing

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“…There are numerous methods that perform this subdivision. The most popular are One against one [24,25,45], One against all [23,26], and Error-correcting output codes (ECOC) [27,46].…”

Section: From Bi-class To Multi-class Problemsmentioning

confidence: 99%

A probabilistic multi-class classifier for structural health monitoring

Mechbal

Uribe

Rébillat

2015

Mechanical Systems and Signal Processing

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract. In this paper, a probabilistic multi-class pattern recognition algorithm is developed for damage detection, localization, and quantification in smart mechanical structures. As these structures can face damages of different severities located at various positions, multi-class classifiers are naturally needed in that context. Furthermore, because of the lack of available data in the damaged state and of environmental effects, the experimentally obtained damage sensitive features may differ from those learned offline by the classifier. A multiclass classifier that provides probabilities associated with each damage severity and location instead of a binary decision is thus greatly desirable in that context. To tackle this issue, we propose an original support vector machine (SVM) multi-class clustering algorithm that is based on a probabilistic decision tree (PDT) and that produces a posteriori probabilities associated with damage existence, location, and severity. Furthermore, the PDT is here built by iteratively subdividing the surface of the structure and thus takes into account the actual structure geometry. The proposed algorithm is very appealing as it combines both the computational efficiency of tree architectures and the classification accuracy of SVMs. The effectiveness of this algorithm is illustrated experimentally on a composite plate instrumented with piezoelectric elements on which damages are simulated using added masses. Damage sensitive features are computed using an active approach based on the permanent emission of non-resonant Lamb waves into the structure and on the recognition of amplitude disturbed diffraction patterns. On the basis of these damage-sensitive features, the proposed multi-class probabilistic classifier generates decisions that are in excellent agreement with the actual severities and locations of the simulated damages.

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A subspace approach to error correcting output codes

Cited by 32 publications

References 19 publications

On the usefulness of one-class classifier ensembles for decomposition of multi-class problems

On the usefulness of one-class classifier ensembles for decomposition of multi-class problems

Intelligent facial emotion recognition using a layered encoding cascade optimization model

A probabilistic multi-class classifier for structural health monitoring

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