Support Vector Machine Ensemble with Bagging

Kim, Hyun-Chul; Pang, Shaoning; Je, Hong-Mo; Kim, Daijin; Bang, Sung Yang

doi:10.1007/3-540-45665-1_31

Cited by 102 publications

(57 citation statements)

References 7 publications

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“…A crucial issue inherent in the weighted combination technique is related to the calculation of the ideal weight of each base classifier [21]. In this technique, the weight is generally determined based on the proportion to the classification accuracies of base classifiers on training data [22]. The formulation employed to decide the weights in classifier fusion for the purposes of this study is given ahead ∑ ( )…”

Section: Constructing a Kfkt Ensemblementioning

confidence: 99%

A supervised discriminant subspaces-based ensemble learning for binary classification

Binol¹,

Cukur²,

Bal³

2016

IJACR

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Section: Constructing a Kfkt Ensemblementioning

confidence: 99%

A supervised discriminant subspaces-based ensemble learning for binary classification

Binol¹,

Cukur²,

Bal³

2016

IJACR

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“…To demonstrate that the better performance of V-SVM is not solely from the assembling of multiple classifiers, two other ensembles of classifiers were used to compare with V-SVM. One is the Bagging classifier [22], where a set of SVM classifiers are trained independently, with each trained based on a randomly chosen subset (here 80%) of training images.…”

Section: Effect Of Video-specific Classifier Trainingmentioning

confidence: 99%

“…The majority voting from all the individual SVM classifiers are used to predict the class of any new image [22]. We call this classifer 'bagging-SVM'.…”

Section: Effect Of Video-specific Classifier Trainingmentioning

confidence: 99%

Video-Specific SVMs for Colonoscopy Image Classification

Manivannan

Wang

Trujillo

et al. 2014

Computer-Assisted and Robotic Endoscopy

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Abstract. We propose a novel classification framework called the videospecific SVM (V-SVM) for normal-vs-abnormal white-light colonoscopy image classification. V-SVM is an ensemble of linear SVMs, with each trained to separate the abnormal images in a particular video from all the normal images in all the videos. Since V-SVM is designed to capture lesion-specific properties as well as intra-class variations it is expected to perform better than SVM. Experiments on a colonoscopy image dataset with about 10, 000 images show that V-SVM significantly improves the performance over SVM and other baseline classifiers.

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“…Indeed, benefiting from bootstrapping and aggregation, bagging [2] lowers both the variance and the bias component of the error. Tailored to SVM [12] it has been shown that notably in the case of multi-class classification SVM ensembles outperform a single SVM in terms of accuracy [18]. …”

Section: Extension To Baggingmentioning

confidence: 99%

Hash-Based Support Vector Machines Approximation for Large Scale Prediction

Litayem¹,

Joly²,

Boujemaa³

2012

Procedings of the British Machine Vision Conference 2012

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How-to train effective classifiers on huge amount of multimedia data is clearly a major challenge that is attracting more and more research works across several communities. Less efforts however are spent on the counterpart scalability issue: how to apply big trained models efficiently on huge non annotated media collections ? In this paper, we address the problem of speeding-up the prediction phase of linear Support Vector Machines via Locality Sensitive Hashing. We propose building efficient hashbased classifiers that are applied in a first stage in order to approximate the exact results and filter the hypothesis space. Experiments performed with millions of one-against-one classifiers show that the proposed hash-based classifier can be more than two orders of magnitude faster than the exact classifier with minor losses in quality.

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Support Vector Machine Ensemble with Bagging

Cited by 102 publications

References 7 publications

A supervised discriminant subspaces-based ensemble learning for binary classification

A supervised discriminant subspaces-based ensemble learning for binary classification

Video-Specific SVMs for Colonoscopy Image Classification

Hash-Based Support Vector Machines Approximation for Large Scale Prediction

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