Ensembles of One Class Support Vector Machines

Shieh, Albert D.; Kamm, David

doi:10.1007/978-3-642-02326-2_19

Cited by 29 publications

(17 citation statements)

References 11 publications

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“…Tax and Duin investigated the influence of feature sets and the types of one-class classifiers for the best choice of the combination rule [30]. A bagging-based one-class support vector machine ensemble method was proposed in [31]. A dynamic ensemble strategy based on structural risk minimization [32] was proposed by Goh et al for multi-class image annotation [7].…”

Section: Ensemble Of One-class Classifiersmentioning

confidence: 99%

One-class kernel subspace ensemble for medical image classification

Zhang

Coenen

et al. 2014

EURASIP J. Adv. Signal Process.

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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.

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Section: Ensemble Of One-class Classifiersmentioning

confidence: 99%

One-class kernel subspace ensemble for medical image classification

Zhang

Coenen

et al. 2014

EURASIP J. Adv. Signal Process.

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“…As OCSVM has been traditionally viewed as stable, bagging of OCSVM is proposed by Albert et al until 2009 [16]. It uses kernel density estimation to compute weights.…”

Section: Bagging Of Ocsvmmentioning

confidence: 99%

Visual Object Recognition with Bagging of One Class Support Vector Machines

Xie

2011

2011 Second International Conference on Innovations in Bio-Inspired Computing and Applications

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A large number of training samples is required in developing visual object recognition systems. However, the size of samples is limited sometimes. This paper investigates bagging of one class support vector machines (OCSVM), which just use one class of objects for training. Experiments are performed on Caltech101 database. Our findings show that the performance with bagging method is better than single OCSVM. Furthermore, bagging of OCSVM can also keep better performance with limited number of training samples.

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“…First, approaches that divide the feature space and train individual one-class classifiers on the different feature subsets [9,5]. Second, approaches that divide the target class data and train individual one-class classifiers on the different object subsets [10,7]. Our approach belongs to the second category and is related to the work of Wang et al [10], who employ an agglomerative hierarchical clustering strategy in order to partition target class data.…”

Section: One-class Classification In Irmentioning

confidence: 99%

Cluster-based one-class ensemble for classification problems in information retrieval

Lipka

Stein

Anderka

2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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A number of relevant information retrieval classification problems are one-class classification problems at heart. I.e., labeled data is only available for one class, the so-called target class, and common discrimination-based classification approaches, be them binary or multiclass, are not applicable. Achieving a high effectiveness when solving one-class problems is difficult anyway and it becomes even more challenging when the target class data is multimodal, which is often the case. To address these concerns we propose a cluster-based one-class ensemble that consists of four steps:(1) applying a clustering algorithm to the target class data, (2) training an individual one-class classifier for each of the identified clusters, (3) aggregating the decisions of the individual classifiers, and (4) selecting the best fitting clustering model. We evaluate our approach with four datasets: an artificially generated dataset, a dataset compiled from a known multiclass text corpus, and two datasets related to one-class problems that received much attention recently, namely authorship verification and quality flaw prediction. Our approach outperforms a one-class SVM on all four datasets.

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Ensembles of One Class Support Vector Machines

Cited by 29 publications

References 11 publications

One-class kernel subspace ensemble for medical image classification

One-class kernel subspace ensemble for medical image classification

Visual Object Recognition with Bagging of One Class Support Vector Machines

Cluster-based one-class ensemble for classification problems in information retrieval

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