Browse > Article
http://dx.doi.org/10.9708/jksci.2018.23.09.007

Effective Multi-label Feature Selection based on Large Offspring Set created by Enhanced Evolutionary Search Process  

Lim, Hyunki (Korea Institute of Science and Technology)
Seo, Wangduk (School of Computer Science and Engineering, Chung-Ang University)
Lee, Jaesung (School of Computer Science and Engineering, Chung-Ang University)
Publication Information
Journal of the Korea Society of Computer and Information / v.23, no.9, 2018 , pp. 7-13 More about this Journal
Abstract
Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.
Keywords
Multi-label Learning; Multi-label Feature Selection; Evolutionary Search; Memetic Offspring Creation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ghasemzadeh, H., Amini, N., Saeedi, R., Sarrafzadeh, M., "Power-aware computing in wearable sensor networks: An optimal feature selection," IEEE Trans. Mob. Comput., Vol. 14, pp. 800-812, 2015.   DOI
2 Yan, K., Zhang, D., "Feature selection and analysis on correlated gas sensor data with recursive feature elimination," Sensors and Actuators B: Chemical, Vol. 212, pp. 353-363, 2015.   DOI
3 Li, D., Zhou, Y., Hu, G., Spanos, C.J., "Optimal Sensor Configuration and Feature Selection for AHU Fault Detection and Diagnosis," IEEE Transactions on Industrial Informatics, Vol. 13, pp. 1369-1380, 2017.   DOI
4 Lee, J., Kim, D.W, "SCLS: Multi-label feature selection based on scalable criterion for large label set," Pattern Recognit. Vol. 66, pp. 342-352, 2017.   DOI
5 Lee, J., Seo, W., Kim, D.W., "Effective Evolutionary Multilabel Feature Selection under a Budget Constraint,", Complexity, pp. 1-14, 2018.
6 Zhang, M.L., Pena, J.M., Robles, V., "Feature selection for multi-label naïve Bayes classification," Inf. Sci., Vol. 179, pp. 3218-3229, 2009.   DOI
7 Yin, J., Tao, T., Xu, J., "A multi-label feature selection algorithm based on multi-objective optimization," Int. Joint Conf. Neural Networks. IEEE, pp. 1-7, 2015.
8 Zhang, Y., Gong, D.W., Sun, X.Y., Guo, Y.N., "A PSO-based multi-objective multi-label feature selection method in classification." Sci. Rep. Vol. 7, pp. 1-12, 2017.   DOI
9 Gupta, P., Dallas, T., "Feature selection and activity recognition system using a single triaxial accelerometer," IEEE Transactions on Biomedical Engineering, Vol. 61, pp. 1780-1786, 2014.   DOI
10 Read, J., "A pruned problem transformation method for multi-label classification," Proc. New Zealand Computer Science Research Student Conf., pp. 143-150, 2008.
11 Spolaor, N., Cherman, E.A., Monard, M.C., Lee, H.D., "A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach," Electron. Notes Theor. Comput. Sci., pp. 135-151, 2013.
12 Lee, J., Kim, D.W., "Memetic feature selection algorithm for multi-label classification," Inf. Sci., pp. 80-96, 2015.
13 Xue, B., Zhang, M., Browne,W.N., Yao, X., "A survey on evolutionary computation approaches to feature selection," IEEE Trans. Evol. Comput., Vol. 20, pp. 606-626, 2016.   DOI
14 Perez, M., Rubin, D.M., Marwala, T., Scott, L.E., Stevens, W., "A population-based incremental learning approach to microarray gene expression feature selection," In Proc. IEEE 26th Conv. Electr. Electron. Eng, Israel., pp. 000010-000014, 2010.
15 Zhou, A., Sun, J., Zhang, Q., "An estimation of distribution algorithm with cheap and expensive local search methods," IEEE Trans. Evol Comput., Vol. 19, pp. 807-822, 2015.   DOI
16 Baluja, S., "Population-based incremental learning. a method for integrating genetic search based function optimization and competitive learning," Technical report, Carnegie-Mellon Univ Pittsburgh Pa Dept Of Computer Science, 1994.
17 Pelikan, M., Goldberg, D.E., Lobo, F.G., "A survey of optimization by building and using probabilistic models," Comput. Optim. Appl., Vol. 21, pp. 5-20, 2002.   DOI
18 Lee, J., Lim, H., Kim, D.W., "Approximating mutual information for multi-label feature selection," Electron. Lett. Vol. 48, No. 15, pp. 929-930, 2012.   DOI
19 Lipowski, A., Lipowska, D., "Roulette-wheel selection via stochastic acceptance," Physica A: Statistical Mechanics and its Applications, Vol. 391, pp. 2193-2196, 2012.   DOI
20 Lee, J., Kim, D.W., "Feature selection for multi-label classification using multivariate mutual information," Pattern Recognit. Lett., Vol. 34, pp. 349-357, 2013.   DOI
21 Wang, Tinghuai, Jean-Yves Guillemaut, and John Collomosse, "Multi-label propagation for coherent video segmentation and artistic stylization," Image Processing (ICIP), 2010 17th IEEE International Conference on, pp. 3005-3008, 2010.
22 Demsar, J., "Statistical comparisons of classifier over multiple data sets," J. Mach. Learn. Res., Vol. 7, pp. 1-30, 2006.
23 Cano, A., Luna, J.M., Gibaja, E.L., Ventura, S., "LAIM discretization for multi-label data," Inf. Sci., pp. 370-384, 2016.
24 Kumar, R., Qamar, I., Virdi, J.S., Krishnan, N.C., "Multi-label Learning for Activity Recognition," Intelligent Environments (IE), 2015 International Conference on. IEEE, pp. 152-155, 2015.
25 Alhamoud, A., Muradi, V., Böhnstedt, D., Steinmetz, R., "Activity recognition in multi-user environments using techniques of multi-label classification," Proceedings of the 6th International Conference on the Internet of Things. ACM, pp. 15-23, 2016.
26 Sousa, R., Gama, J., "Multi-label classification from high-speed data streams with adaptive model rules and random rules," Progress in Artificial Intelligence, pp. 1-11, 2018.
27 Read, J., Bifet, A., Holmes, G., Pfahringer, B., "Scalable and efficient multi-label classification for evolving data streams," Machine Learning, Vol. 88, pp. 243-272.
28 Read, J., Martino, L., Olmos, P.M., Luengo, D., "Scalable multi-output label prediction: From classifier chains to classifier trellises," Pattern Recognition, Vol. 48, pp. 2096-2109, 2012.
29 Park, Jong-Beom, "Development of the Poker Game Achievement Engine for Artificial Intelligence," Journal of the Korea Society of Computer and Information, Vol. 14, No. 11, pp. 41-52, 2009.
30 Persing, Isaac, and Vincent Ng, "Modeling thesis clarity in student essays," Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Vol. 1, pp. 260-269, 2013.