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2016 | OriginalPaper | Chapter

Feature and Search Space Reduction for Label-Dependent Multi-label Classification

Authors : Prema Nedungadi, H. Haripriya

Published in: Proceedings of the Second International Conference on Computer and Communication Technologies

Publisher: Springer India

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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.

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Literature
1.
Smith, L.I.: A tutorial on principal components analysis. Cornell Univ. USA 51, 52 (2002)
2.
Hotelling, H. Relations between two sets of variates. Biometrika, 321–377 (1936).
3.
De Leeuw, J. (2011). History of nonlinear principal component analysis
4.
Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Trans. Comput. 18(5), 401–409 (1969)CrossRef
5.
Nedungadi, P., Harikumar, H., Ramesh, M.: A high performance hybrid algorithm for text classification. In 5th International Conference on the Applications of Digital Information and Web Technologies (ICADIWT), IEEE, pp. 118–123 (2014)
6.
Nedungadi, P., Haripriya, H.: Exploiting label dependency and feature similarity for multi-label classification. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI) IEEE, pp. 2196–2200 (2014)
7.
Zhang, M.L., Zhou, Z.H.: ML-KNN: A lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)MATHCrossRef
8.
Chiang, T.H., Lo, H.Y., Lin, S.D.: A Ranking-based KNN Approach for Multi-Label Classification. In: ACML. pp. 81–96 (2012)
9.
Clare, A., King, R. D.: Knowledge discovery in multi-label phenotype data. In: Principles of data mining and knowledge discovery, pp. 42–53, Springer Berlin Heidelberg (2001)
10.
Schapire, R.E., Singer, Y.: BoosTexter: a boosting-based system for text categorization. Mach. Learn. 39(2–3), 135–168 (2000)MATHCrossRef
11.
Charte, F., Rivera, A., del Jesus, M.J., Herrera, F.: Improving multi-label classifiers via label reduction with association rules. In: Hybrid Artificial Intelligent Systems, pp. 188–199, Springer Berlin Heidelberg (2012)
12.
Zhang, M. L., Wu, L. (2011). LIFT: Multi-label learning with label-specific features
13.
Hang, L.I.: A short introduction to learning to rank. IEICE Trans. Inform. Syst. 94(10), 1854–1862 (2011)
14.
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM. pp. 133–142 (2002)
15.
Ji, S., Ye, J.: Linear Dimensionality Reduction for Multi-label Classification. In: IJCAI 9, pp. 1077–1082 (2009)
16.
Qian, B., Davidson, I.: Semi-Supervised Dimension Reduction for Multi-Label Classification. In AAAI. 10, pp. 569–574 (2010)
17.
Zhang, Y., Zhou, Z.H.: Multilabel dimensionality reduction via dependence maximization. ACM Trans. Knowl. Disc. Data (TKDD) 4(3), 14 (2010)
18.
Zhou, T., Tao, D.: Multi-label subspace ensemble. In International Conference on Artificial Intelligence and Statistics. pp. 1444–1452 (2012)
19.
Wei, Z., Zhang, H., Zhang, Z., Li, W., Miao, D.: A naive Bayesian multi-label classification algorithm with application to visualize text search results. Int. J. Adv. Intell. 3(2), 173–188 (2011)
20.
Cao, Y., Xu, J., Liu, T.Y., Li, H., Huang, Y., Hon, H.W.: Adapting ranking SVM to document retrieval. In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, ACM pp. 186–193 (2006)
21.
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
22.
Prajapati, P., Thakkar, A., Ganatra, A.: A survey and current research challenges in multi-label classification methods. Int. J. Soft Comput. 2 (2012)
23.
Cheng, W., Hüllermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Mach. Learn. 76(2–3), 211–225 (2009)CrossRef
24.
Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079–1089 (2011)CrossRef
25.
Rätsch, G., Onoda, T., Müller, K.R.: Soft margins for AdaBoost. Mach. Learn. 42(3), 287–320 (2001)MATHCrossRef
26.
Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Mining text data. pp. 163–222, Springer US
Metadata
Title
Feature and Search Space Reduction for Label-Dependent Multi-label Classification
Authors
Prema Nedungadi
H. Haripriya
Copyright Year
2016
Publisher
Springer India
Book
Proceedings of the Second International Conference on Computer and Communication Technologies

Print ISBN: 978-81-322-2522-5

Electronic ISBN: 978-81-322-2523-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-81-322-2523-2_57