Unbalanced Decision Trees for multi-class classification

Ramanan, Amirthalingam; Suppharangsan, Somjet; Niranjan, Mahesan

doi:10.1109/iciinfs.2007.4579190

Cited by 26 publications

(17 citation statements)

References 12 publications

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“…Table 9 presents the maximum overall accuracies of the HMC-LAD compared to the supervised learning approaches for the six datasets. In case of DAG-SVM and UDT-SVM, we used the experimental result in the work of Ramanan et al (2007). The maximum overall classification accuracies of HMC-LAD yielded the best performance in five of the six datasets, except for the Dermatology dataset.…”

Section: (A) Irismentioning

confidence: 98%

“…To compensate these limitations, OvA-type method has been introduced recently, where it divides a node with K classes into two sub-nodes with 1 and ðK À 1Þ classes, respectively. It showed high computational efficiency and classification accuracy compared to HvH-type method (Ramanan, Suppharangsan, & Niranjan, 2007;Sidaoui & Sadouni, 2014;Wu, Lee, & Yang, 2008;Yang, Yu, He, & Guo, 2013).…”

Section: Introductionmentioning

confidence: 94%

“…DAG-SVM and UDT-SMV are SVM-based hierarchical multi-class classification methods. They use SVM for binary tree partition at each node and UDT-SVM uses OvA-type binary tree (Ramanan et al, 2007). SMO implements the sequential minimal-optimization algorithm for training a support vector classifier using kernel functions such as polynomial or Gaussian kernels, where pairwise classification is used for multi-class classification problems (Platt, 1999).…”

Section: (A) Irismentioning

confidence: 99%

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Hierarchical multi-class LAD based on OvA-binary tree using genetic algorithm

Kim

Choi

2015

Expert Systems with Applications

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Section: (A) Irismentioning

confidence: 98%

Section: Introductionmentioning

confidence: 94%

Section: (A) Irismentioning

confidence: 99%

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Hierarchical multi-class LAD based on OvA-binary tree using genetic algorithm

Kim

Choi

2015

Expert Systems with Applications

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“…The goal is to construct a function which, given a new data point, will correctly predict the class to which the new data point belongs. There are four standard techniques frequently employed by SVMs to tackle multiclass problems, namely One-Versus-One (OVO) [11], One-Versus-All (OVA) [12], Directed Acyclic Graph (DAG) [13], and Unbalanced Decision Tree (UDT) [14]. We used the one-versus-all (OVA) linear SVMs.…”

Section: E Multi-class Classificationmentioning

confidence: 99%

Image Classification of Paddy Field Insect Pests Using Gradient-Based Features

Venugoban¹,

Ramanan²

2014

IJMLC

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“…Subsequently, the results from different classifiers are combined in order to reach a final decision. Interested readers are referred to (Ramanan et al, 2007) for detailed discussions of the OVO and OVA approaches. However, combining results from classifiers that are trained on different sub-problems may result in classification errors (Wang and Yao, 2012).…”

Section: Introductionmentioning

confidence: 99%

SCUT: Multi-Class Imbalanced Data Classification using SMOTE and Cluster-based Undersampling

Agrawal

Viktor

Paquet

2015

Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. Questions? Contact the NRC Publications Archive team atPublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. If you wish to email the authors directly, please see the first page of the publication for their contact information. NRC Publications Archive Archives des publications du CNRCThis publication could be one of several versions: author's original, accepted manuscript or the publisher's version. / La version de cette publication peut être l'une des suivantes : la version prépublication de l'auteur, la version acceptée du manuscrit ou la version de l'éditeur. For the publisher's version, please access the DOI link below./ Pour consulter la version de l'éditeur, utilisez le lien DOI ci-dessous.http://doi.org/10.5220/0005595502260234Access and use of this website and the material on it are subject to the Terms and Conditions set forth at SCUT: multi-class imbalanced data classification using SMOTE and cluster-based undersampling Agrawal, Astha; Viktor, Herna L.; Paquet, Eric http://nparc.cisti-icist.nrc-cnrc.gc.ca/fra/droits L'accès à ce site Web et l'utilisation de son contenu sont assujettis aux conditions présentées dans le site LISEZ CES CONDITIONS ATTENTIVEMENT AVANT D'UTILISER CE SITE WEB. NRC Publications Record / Notice d'Archives des publications de CNRC:http://nparc.cisti-icist.nrc-cnrc.gc.ca/eng/view/object/?id=e8c7556d-9f94-466f-a1e5-72cdf9b9513f http://nparc.cisti-icist.nrc-cnrc.gc.ca/fra/voir/objet/?id=e8c7556d-9f94-466f-a1e5-72cdf9b9513f Abstract: Class imbalance is a crucial problem in machine learning and occurs in many domains. Specifically, the two-class problem has received interest from researchers in recent years, leading to solutions for oil spill detection, tumour discovery and fraudulent credit card detection, amongst others. However, handling class imbalance in datasets that contains multiple classes, with varying degree of imbalance, has received limited attention. In such a multi-class imbalanced dataset, the classification model tends to favour the majority classes and incorrectly classify instances from the minority classes as belonging to the majority classes, leading to poor predictive accuracies. Further, there is a need to handle both the imbalances between classes as well as address the selection of examples within a class (i.e. the so-called within class imbalance). In this paper, we propose the SCUT hybrid sampling method, which is used to balance the number of training examples in such a multi-class setting. Our SCUT approach oversamples minority class examples through the generation of synthetic examples and employs cluster analysis in order to undersample majority classes. In addition, it handles both within-class and between-class imbalance. Our exp...

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Unbalanced Decision Trees for multi-class classification

Abstract: Abstract

Cited by 26 publications

References 12 publications

Hierarchical multi-class LAD based on OvA-binary tree using genetic algorithm

Hierarchical multi-class LAD based on OvA-binary tree using genetic algorithm

Image Classification of Paddy Field Insect Pests Using Gradient-Based Features

SCUT: Multi-Class Imbalanced Data Classification using SMOTE and Cluster-based Undersampling

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