Publication Type:

Book Chapter

Source:

Proceedings of the Second International Conference on Computer and Communication Technologies: IC3T 2015, Volume 2, Springer India, New Delhi, p.591–599 (2016)

ISBN:

9788132225232

URL:

http://dx.doi.org/10.1007/978-81-322-2523-2_57

Keywords:

Hybrid kNN, Multi-label, multiple regression, PCA

Abstract:

The problem of high dimensionality in multi-label domain is an emerging research area to explore. A strategy is proposed to combine both multiple regression and hybrid k-Nearest Neighbor algorithm in an efficient way for high-dimensional multi-label classification. The hybrid kNN performs the dimensionality reduction in the feature space of multi-labeled data in order to reduce the search space as well as the feature space for kNN, and multiple regression is used to extract label-dependent information from the label space. Our multi-label classifier incorporates label dependency in the label space and feature similarity in the reduced feature space for prediction. It has various applications in different domains such as in information retrieval, query categorization, medical diagnosis, and marketing.

Cite this Research Publication

P. Prof. Nedungadi and Haripriya, H., “Feature and Search Space Reduction for Label-Dependent Multi-label Classification”, in Proceedings of the Second International Conference on Computer and Communication Technologies: IC3T 2015, Volume 2, S. Chandra Satapathy, K. Raju, S., Mandal, J. Kumar, and Bhateja, V. New Delhi: Springer India, 2016, pp. 591–599.

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