Abstract
In this paper, we propose a modified supervised adaptive resonance theory neural network, namely Fuzzy ARTMAP (FAM), to undertake multi-label data classification tasks. FAM is integrated with the binary relevance (BR) technique to form BR-FAM. The effectiveness of BR-FAM is evaluated using two benchmark multi-label data classification problems. Its results are compared with those other methods in the literature. The performance of BR-FAM is encouraging, which indicate the potential of FAM-based models for handling multi-label data classification tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37, 1757–1771 (2004)
Tamaazousti, Y., Le Borgne, H., Popescu, A.: Constrained local enhancement of semantic features by content-based sparsity. In: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, ICME 2016, pp. 119–126. ACM, New York (2016)
Li, X., Huo, Y., Jin, Q., Xu., J.: Detecting violence in video using subclasses. In: Proceedings of the 2016 ACM on Multimedia Conference, MM 2016, pp. 586–590. ACM, Amsterdam (2016)
Chávez-MartÃnez, G., Ruiz-Correa, S., Gatica-Perez, D.: Happy and agreeable?: multi-label classification of impressions in social video. In: Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia, MUM 2015, pp. 109–120. ACM, Austria (2015)
Lin, Y.-C., Yang, Y.-H., Chen, Homer H.: Exploiting online music tags for music emotion classification. ACM Trans. Multimedia Comput. Commun. Appl. 7Â s, Article 26 (2011)
Yu, G., Rangwala, H., Domeniconi, C., Zhang, G., Yu, Z.: Protein Function Prediction with Incomplete Annotations. IEEE/ACM Trans. Comput. Biol. Bioinf. 11, 579–591 (2014)
Zhang, M.-L., Zhou, Z.-H.: ML-kNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40, 2038–2048 (2007)
Xu, J.: An extended one-versus-rest support vector machine for multi-label classification. Neurocomputing 74, 3114–3124 (2011)
Xu, J.: Multi-label core vector machine with a zero label. Pattern Recogn. 47, 2542–2557 (2014)
Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., Rosen, D.B.: Fuzzy artmap: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans. Neural Netw. 3, 698–713 (1992)
Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. Lecture Notes in Artificial Intelligence 3056, 22–30 (2004)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3, 1–13 (2007)
Hüllermeier, E., Fürnkranz, J., Cheng, W., Bringer, K.: Label ranking by learning pairwise preferences. Artif. Intell. 172, 1897–1916 (2008)
Fürnkranz, J., Hüllermeier, E., Mencia, L., Brinker, K.: Multi-label classification via calibrated label ranking. Mach. Learn. 73, 133–153 (2008)
Cherman, E.A., Monard, M.C., Metz, J.: Multi-label problem transformation methods: a case study. CLEI Electron. J. 14, 4 (2011)
Tanaka, E.A., Nozawa, S.R., Macedo, A.A., Baranauskas, J.A.: A multi-label approach using binary relevance and decision tree applied to functional genomics. J. Biomed. Inf. 53, 85–95 (2015)
Chou, S., Hsu, C.-L.: MMDT: a multi-valued and multi-labeled decision tree classifier for data mining. Expert Syst. Appl. 28, 799–812 (2005)
Wu, Q., Ye, Y., Zhang, H. Chow, Tommy W.S., Ho, S.-S.: ML-TREE: a tree-structure-based approach to multilabel learning. IEEE Trans. Neural Netw. Learn. Syst. 26, 430–443 (2015)
Chen, W.-J., Shao, Y.-H., Li, C.-N., Deng, N.-Y.: MLTSVM: a novel twin support vector machine to multi-label. Learning 52, 61–74 (2016)
Zhang, M.-L., Zhou, Z.-H.: Multi-label neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18, 1338–1351 (2006)
Chen, Z., Chi, Z., Fu, H., Feng, D.: Multi-instance multi-label image classification: a neural approach. Neurocomputing 99, 298–306 (2013)
van Rijsbergen, C.J.: Information Retrieval. Butterworths, London (1979)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23, 1079–1089 (2011)
Yang, Y.: An evaluation of statistical approaches to text categorization. J. Inf. Retrieval 1, 78–88 (1999)
Mulan: a Java library for multi-label learning. http://mulan.sourceforge.net/datasets-mlc.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Yuan, L.X., Tan, S.C., Goh, P.Y., Lim, C.P., Watada, J. (2018). Fuzzy ARTMAP with Binary Relevance for Multi-label Classification. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-59424-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-59424-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59423-1
Online ISBN: 978-3-319-59424-8
eBook Packages: EngineeringEngineering (R0)