Publication Type:

Conference Paper

Source:

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2014)

ISBN:

9781479930807

URL:

http://ieeexplore.ieee.org/abstract/document/6968582/

Keywords:

feature similarity, Information Retrieval, k-nearest neighbor, kNN, kNN algorithm, label dependency, label set prediction, label space, learning (artificial intelligence), Marketing, Medical diagnosis, multilabel, multilabel classification, multiple regression, multiple rgression, Pattern classification, Prediction algorithms, query categorization, Regression analysis

Abstract:

Multi-label classification is an emerging research area in which an object may belong to more than one class simultaneously. Existing methods either consider feature similarity or label similarity for label set prediction. We propose a strategy to combine both k-Nearest Neighbor (kNN) algorithm and multiple regression in an efficient way for multi-label classification. kNN works well in feature space and multiple regression works well for preserving label dependent information with generated models for labels. Our classifier incorporates feature similarity in the feature space and label dependency in the label space for prediction. It has a wide range of applications in various domains such as in information retrieval, query categorization, medical diagnosis and marketing.

Cite this Research Publication

P. Prof. Nedungadi and Haripriya, H., “Exploiting label dependency and feature similarity for multi-label classification”, in 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014.

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