Publication Type : Book Chapter
Thematic Areas : Learning-Technologies
Publisher : Proceedings of the Second International Conference on Computer and Communication Technologies: IC3T 2015.
Source : Proceedings of the Second International Conference on Computer and Communication Technologies: IC3T 2015, Volume 2, Springer India, New Delhi, p.591–599 (2016)
Url : http://dx.doi.org/10.1007/978-81-322-2523-2_57
ISBN : 9788132225232
Keywords : Hybrid kNN, Multi-label, multiple regression, PCA
Campus : Amritapuri
School : School of Engineering
Center : Amrita Center For Research in Analytics, AmritaCREATE
Department : Computer Science
Year : 2016
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 : Prof. Prema 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., Eds. New Delhi: Springer India, 2016, pp. 591–599
