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Journal of Sensors
Volume 2018, Article ID 3419213, 12 pages
https://doi.org/10.1155/2018/3419213
Research Article

Evolutionary Multilabel Feature Selection Using Promising Feature Subset Generation

School of Computer Science and Engineering, Chung-Ang University, 221, Heukseok-Dong, Dongjak-Gu, Seoul 06974, Republic of Korea

Correspondence should be addressed to Dae-Won Kim; rk.ca.uac@mikwd

Received 8 June 2018; Accepted 7 August 2018; Published 18 September 2018

Academic Editor: Grigore Stamatescu

Copyright © 2018 Jaesung Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Qadri, D. M. Khan, S. F. Qadri et al., “Multisource data fusion framework for land use/land cover classification using machine vision,” Journal of Sensors, vol. 2017, Article ID 3515418, 8 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Alhamoud, V. Muradi, D. Bohnstedt, and R. Steinmetz, “Activity recognition in multi-user environments using techniques of multi-label classification,” in Proceedings of the 6th International Conference on the Internet of Things - IoT'16, pp. 15–23, Stuttgart, Germany, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. R. Gravina, P. Alinia, H. Ghasemzadeh, and G. Fortino, “Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges,” Information Fusion, vol. 35, pp. 68–80, 2017. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Du, Z. Wang, L. Zhang, L. Zhang, and D. Tao, “Robust and discriminative labeling for multi-label active learning based on maximum correntropy criterion,” IEEE Transactions on Image Processing, vol. 26, no. 4, pp. 1694–1707, 2017. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Ghasemzadeh, N. Amini, R. Saeedi, and M. Sarrafzadeh, “Power-aware computing in wearable sensor networks: an optimal feature selection,” IEEE Transactions on Mobile Computing, vol. 14, no. 4, pp. 800–812, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Li, Y. Zhou, G. Hu, and C. J. Spanos, “Optimal sensor configuration and feature selection for AHU fault detection and diagnosis,” IEEE Transactions on Industrial Informatics, vol. 13, no. 3, pp. 1369–1380, 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. K. Yan and D. Zhang, “Feature selection and analysis on correlated gas sensor data with recursive feature elimination,” Sensors and Actuators B: Chemical, vol. 212, pp. 353–363, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Cheng, Z. Cai, J. Li, and H. Gao, “Extracting kernel dataset from big sensory data in wireless sensor networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 4, pp. 813–827, 2017. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Kumar, I. Qamar, J. S. Virdi, and N. C. Krishnan, “Multi-label learning for activity recognition,” in 2015 International Conference on Intelligent Environments, pp. 152–155, Prague, Czech Republic, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Read, L. Martino, P. M. Olmos, and D. Luengo, “Scalable multi-output label prediction: from classifier chains to classifier trellises,” Pattern Recognition, vol. 48, no. 6, pp. 2096–2109, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. M.-L. Zhang and Z.-H. Zhou, “A review on multi-label learning algorithms,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 8, pp. 1819–1837, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. T. Liu, Y. Chen, D. Li, and M. Wu, “An active feature selection strategy for DWT in artificial taste,” Journal of Sensors, vol. 2018, Article ID 9709505, 11 pages, 2018. View at Publisher · View at Google Scholar
  13. X. Teng, H. Dong, and X. Zhou, “Adaptive feature selection using v-shaped binary particle swarm optimization,” PLoS One, vol. 12, no. 3, article e0173907, 2017. View at Publisher · View at Google Scholar · View at Scopus
  14. M.-L. Zhang, J. M. Pena, and V. Robles, “Feature selection for multi-label naive Bayes classification,” Information Sciences, vol. 179, no. 19, pp. 3218–3229, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Lee and D.-W. Kim, “SCLS: multi-label feature selection based on scalable criterion for large label set,” Pattern Recognition, vol. 66, pp. 342–352, 2017. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. Michalewicz and D. B. Fogel, How to Solve It: Modern Heuristics, Springer Science & Business Media, 2013.
  17. J. Lee and D.-W. Kim, “Memetic feature selection algorithm for multi-label classification,” Information Sciences, vol. 293, pp. 80–96, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Lee, W. Seo, and D.-W. Kim, “Effective evolutionary multilabel feature selection under a budget constraint,” Complexity, vol. 2018, Article ID 3241489, 14 pages, 2018. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Zhou, J. Sun, and Q. Zhang, “An estimation of distribution algorithm with cheap and expensive local search methods,” IEEE Transactions on Evolutionary Computation, vol. 19, no. 6, pp. 807–822, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Lee and D.-W. Kim, “Feature selection for multi-label classification using multivariate mutual information,” Pattern Recognition Letters, vol. 34, no. 3, pp. 349–357, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Lee and D.-W. Kim, “Mutual information-based multi-label feature selection using interaction information,” Expert Systems with Applications, vol. 42, no. 4, pp. 2013–2025, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Read, “A pruned problem transformation method for multi-label classification,” in Proceedings of New Zealand Computer Science Research Student Conference, pp. 143–150, Christchurch, New Zealand, 2008. View at Google Scholar
  23. N. Spolaor, E. A. Cherman, M. C. Monard, and H. D. Lee, “A comparison of multi-label feature selection methods using the problem transformation approach,” Electronic Notes in Theoretical Computer Science, vol. 292, pp. 135–151, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. B. Xue, M. Zhang, W. N. Browne, and X. Yao, “A survey on evolutionary computation approaches to feature selection,” IEEE Transactions on Evolutionary Computation, vol. 20, no. 4, pp. 606–626, 2016. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Yin, T. Tao, and J. Xu, “A multi-label feature selection algorithm based on multi-objective optimization,” in 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–7, Killarney, Ireland, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. Y. Zhang, D.-w. Gong, X.-y. Sun, and Y.-n. Guo, “A PSO-based multi-objective multi-label feature selection method in classification,” Scientific Reports, vol. 7, no. 1, p. 376, 2017. View at Publisher · View at Google Scholar · View at Scopus
  27. X. Chen, W. Liu, F. Su, and G. Zhou, “Semisupervised multiview feature selection for VHR remote sensing images with label learning and automatic view generation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 6, pp. 2876–2888, 2017. View at Publisher · View at Google Scholar · View at Scopus
  28. P. Gupta and T. Dallas, “Feature selection and activity recognition system using a single triaxial accelerometer,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 6, pp. 1780–1786, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Jain and D. Zongker, “Feature selection: evaluation, application, and small sample performance,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp. 153–158, 1997. View at Publisher · View at Google Scholar · View at Scopus
  30. K. Yan, L. Ma, Y. Dai, W. Shen, Z. Ji, and D. Xie, “Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis,” International Journal of Refrigeration, vol. 86, pp. 401–409, 2018. View at Publisher · View at Google Scholar · View at Scopus
  31. Y. Luo, Y. Duan, W. Li, P. Pace, and G. Fortino, “A novel mobile and hierarchical data transmission architecture for smart factories,” IEEE Transactions on Industrial Informatics, vol. 14, no. 8, pp. 3534–3546, 2018. View at Publisher · View at Google Scholar · View at Scopus
  32. R. Islam, S. A. Khan, and J.-m. Kim, “Discriminant feature distribution analysis-based hybrid feature selection for online bearing fault diagnosis in induction motors,” Journal of Sensors, vol. 2016, Article ID 7145715, 16 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  33. M. Perez, D. M. Rubin, T. Marwala, L. E. Scott, and W. Stevens, “A population-based incremental learning approach to microarray gene expression feature selection,” in 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, pp. 10–14, Eilat, Israel, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Baluja, “Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning,” Tech. Rep., Technical Report Carnegie-Mellon University Pittsburgh PA Department of Computer Science, 1994. View at Google Scholar
  35. Z. Zhu, S. Jia, and Z. Ji, “Towards a memetic feature selection paradigm [application notes],” IEEE Computational Intelligence Magazine, vol. 5, no. 2, pp. 41–53, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. M. Pelikan, D. E. Goldberg, and F. G. Lobo, “A survey of optimization by building and using probabilistic models,” Computational Optimization and Applications, vol. 21, no. 1, pp. 5–20, 2002. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Lipowski and D. Lipowska, “Roulette-wheel selection via stochastic acceptance,” Physica A: Statistical Mechanics and its Applications, vol. 391, no. 6, pp. 2193–2196, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. J. Lee, H. Lim, and D.-W. Kim, “Approximating mutual information for multi-label feature selection,” Electronics Letters, vol. 48, no. 15, pp. 929-930, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. A. Cano, J. M. Luna, E. L. Gibaja, and S. Ventura, “LAIM discretization for multi-label data,” Information Sciences, vol. 330, pp. 370–384, 2016. View at Publisher · View at Google Scholar · View at Scopus
  40. J. Demsar, “Statistical comparisons of classifier over multiple data sets,” Journal of Machine Learning Research, vol. 7, pp. 1–30, 2006. View at Google Scholar
  41. O. J. Dunn, “Multiple comparisons among means,” Journal of the American Statistical Association, vol. 56, no. 293, pp. 52–64, 1961. View at Publisher · View at Google Scholar · View at Scopus