Emphatic Constraints Support Vector Machine

Sabzekar, Mostafa; Yazdi, Hadi Sadoghi; Naghibzadeh, Mahmoud; Effati, Sohrab

doi:10.7763/ijcee.2010.v2.152

IJCEE

2010

DOI: 10.7763/ijcee.2010.v2.152

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Emphatic Constraints Support Vector Machine

Mostafa Sabzekar¹,

Hadi Sadoghi Yazdi²,

Mahmoud Naghibzadeh³

et al.

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Cited by 2 publications

References 23 publications

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Dual Membership Fuzzy Support Vector Machine Algorithm Based on SVDD

Feng

Pei

et al. 2020

J. Phys.: Conf. Ser.

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In the case of excessive overlap between positive and negative samples in data set, the deviation in the category of reconstructed sample points will lead to unsatisfactory discrimination of SVM, no matter what methods are used to reconstruct the sample set. A dual membership fuzzy support vector machine algorithm based on support vector data domain description was thus proposed, followed by a simulation analysis of common data set. Experimental results show that the proposed algorithm can work well in classification when the sample set is overlapped.

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Dual Membership Fuzzy Support Vector Machine Algorithm Based on SVDD

Feng

Pei

et al. 2020

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

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Classification in the Presence of Label Noise: A Survey

Frénay‬

Verleysen

2014

IEEE Trans. Neural Netw. Learning Syst.

1,447

917

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Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.

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Emphatic Constraints Support Vector Machine

Cited by 2 publications

References 23 publications

Dual Membership Fuzzy Support Vector Machine Algorithm Based on SVDD

Dual Membership Fuzzy Support Vector Machine Algorithm Based on SVDD

Classification in the Presence of Label Noise: A Survey

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