Combining different biometric traits with one-class classification

Bergamini, Carlo; Oliveira, Luiz S.; Koerich, Alessandro L.; Sabourin, Robert

doi:10.1016/j.sigpro.2009.04.043

Cited by 44 publications

(38 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Perdisci et al [33] also used an ensemble of one-class SVMs to create a 'high-speed payload-based' anomaly detection system, in which the features were first extracted and clustered and the OCSVM ensemble was then constructed based on the clustered feature subsets. A biometric classification system combining different biometric features was proposed by Bergamini et al [8], where the one-class SVMs in the ensemble were trained by the data from different people. The feature subset strategy provides diversity with respect to the base classifiers, and some researchers emphasize the importance of measuring diversity in ensembles so as to improve classification performance [9,34].…”

Section: Ensemble Of One-class Classifiersmentioning

confidence: 99%

“…The main idea behind the ensemble methodology is to use several classifiers and combine the individual results in order to produce a classification that outperforms the outcome that would http://asp.eurasipjournals.com/content/2014/1/17 have been produced if the classifiers were to operate in isolation [6]. Ensembles of one-class classifiers have also been shown to perform better than individual classifiers [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

One-class kernel subspace ensemble for medical image classification

Zhang

Coenen

et al. 2014

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product combining rule was used for combining the KPCA models to produce classification confidence scores for assigning an image to each class. The effectiveness of the proposed classification scheme was verified using a breast cancer biopsy image dataset and a 3D optical coherence tomography (OCT) retinal image set. The combination of different image features exploits the complementary strengths of these different feature extractors. The proposed classification scheme obtained promising results on the two medical image sets. The proposed method was also evaluated on the UCI breast cancer dataset (diagnostic), and a competitive result was obtained.

show abstract

Section: Ensemble Of One-class Classifiersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

One-class kernel subspace ensemble for medical image classification

Zhang

Coenen

et al. 2014

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…As principais estratégias nessa linha incluem o autoassociator (Japkowicz, 2001;Manevitz and Yousef, 2007) e one-class SVMs (Schölkopf et al, 2001;Raskutti and Kowalczyk, 2004;Manevitz and Yousef, 2002;Bergamini et al, 2009 (Fan et al, 1999), CSB1 e CSB2 (Ting, 2000) e, AdaC1, AdaC2 e AdaC3 (Sun et al, 2007). Um estudo empírico envolvendo a aplicação desses métodos a vários problemas reais de diagnóstico mé-dico foi conduzido por Sun et al (2007 Nessa revisão, uma maior atenção é dedicada às soluções dessa última classe e assim, uma descrição detalhada dos principais trabalhos propostos no âmbito de máquinas de kernel e Redes Neurais Artificiais (RNAs) é fornecida nas seções a seguir.…”

Section: Adaptações Em Algoritmos De Aprendizadounclassified

Aprendizado supervisionado com conjuntos de dados desbalanceados

Castro

Braga

2011

Sba Controle & Automação

View full text Add to dashboard Cite

Supervised Learning with Imbalanced Data Sets: An OverviewTraditional learning algorithms induced by complex and highly imbalanced training sets may have difficulty in distinguishing between examples of the groups. The tendency is to create classification models that are biased toward the overrepresented (majority) class, resulting in a low rate of recognition for the minority group. This paper provides a survey of this problem which has attracted the interest of many researchers in recent years. In the scope of two-class classification tasks, concepts related to the nature of the imbalanced class problem and evaluation metrics are presented, including the foundations of the ROC (Receiver Operating Characteristic) analysis; plus a state of the art of the proposed solutions. At the end of the paper a brief discussion on how the subject can be extended to multiclass learning is provided.KEYWORDS: imbalanced data sets, supervised learning, evaluation metrics, ROC analysis, resampling methods, costsensitive approach. RESUMOAlgoritmos de aprendizado tradicionais induzidos por conjuntos de treinamento complexos e altamente desbalanceados têm apresentado dificuldade em diferenciar entre os grupos. A tendência é produzir modelos (ou regras) de classificação que favorecem a classe com maior probabilidade de ocorrên-cia (majoritária), resultando em uma baixa taxa de reconhecimento para o grupo minoritário. O objetivo desse artigo é fornecer uma investigação sobre esse problema, que tem atraído o interesse de muitos pesquisadores nos últimos anos. No escopo de tarefas de classificação binária, são apresentados conceitos associados à natureza do problema de classes desbalanceadas e métricas de avaliação, incluindo os fundamentos da análise ROC (Receiver Operating Characteristic); além do estado da arte das soluções propostas na literatura. Uma breve discussão a respeito de como os tópicos abordados no artigo podem ser estendidos para o aprendizado multiclasse é também fornecida. PALAVRAS-CHAVE:classes desbalanceadas, aprendizado supervisionado, métricas de avaliação, análise ROC, méto-dos de reamostragem, abordagem sensível ao custo.

show abstract

“…Hence, the OCC has been successfully employed in many applications such as image retrieval [2], automated document retrieval and classification [3] and combining different biometric traits [4]. Nowadays, extended multi-class implementation to new classes is strongly required for instance, in biometric identification.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Integral Combination of One-Class Classifiers Designed for Multi-class Classification

Hadjadji

Chibani

Nemmour

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. One-Class Classifier (OCC) has been widely used for its ability to learn without counterexamples. Its main advantage for multi-class is offering an open system and therefore allows easily extending new classes without retraining OCCs. Generally, pattern recognition systems designed by a single source of information suffer from limitations such as the lack of uniqueness and nonuniversality. Thus, combining information from multiple sources becomes a mode for designing pattern recognition systems. Usually, fixed rules such as average, product, minimum and maximum are the standard used combiners for OCC ensembles. However, fixed combiners cannot be useful to treat some difficult cases. Hence, we propose in this paper a combination scheme of OCCs based on the use of fuzzy integral (FI) operators. Experimental results conducted on different types of OCC and two different handwritten datasets prove the superiority of FI against fixed combiners for an open multi-class classification based on OCC ensemble.

show abstract

Combining different biometric traits with one-class classification

Cited by 44 publications

References 26 publications

One-class kernel subspace ensemble for medical image classification

One-class kernel subspace ensemble for medical image classification

Aprendizado supervisionado com conjuntos de dados desbalanceados

Fuzzy Integral Combination of One-Class Classifiers Designed for Multi-class Classification

Contact Info

Product

Resources

About