[1] X. Qin, Y. Gu, Data fusion in the Internet of Things, Procedia Engineering, 15 (2011) 3023-3026.
[2] H.Y. Shwe, X.-H. Jiang, S. Horiguchi, Energy saving in wireless sensor networks, Journal of Communication and Computer, 6(5) (2009) 20-27.
[3] G. Anastasi, M. Conti, M. Di Francesco, A. Passarella, Energy conservation in wireless sensor networks: A survey, Ad hoc networks, 7(3) (2009) 537-568.
[4] M. Lewitt, R. Polikar, An ensemble approach for data fusion with Learn++, Multiple Classifier Systems, (2003) 161-161.
[5] W.-T. Sung, M.-H. Tsai, Data fusion of multi-sensor for IOT precise measurement based on improved PSO algorithms, Computers & Mathematics with Applications, 64(5) (2012) 1450-1461.
[6] J. Zhou, L. Hu, F. Wang, H. Lu, K. Zhao, An efficient multidimensional fusion algorithm for IoT data based on partitioning, tsinghua science and technology, 18(4) (2013) 369-378.
[7] A.R. Pinto, C. Montez, G. Araújo, F. Vasques, P. Portugal, An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms, Information fusion, 15 (2014) 90-101.
[8] R. Gravina, P. Alinia, H. Ghasemzadeh, G. Fortino, Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges, Information Fusion, 35 (2017) 68-80.
[9] M.M. Fouad, N.E. Oweis, T. Gaber, M. Ahmed, V. Snasel, Data mining and fusion techniques for WSNs as a source of the big data, Procedia Computer Science, 65 (2015) 778-786.
[10] M. Marjani, F. Nasaruddin, A. Gani, A. Karim, I.A.T. Hashem, A. Siddiqa, I. Yaqoob, Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges, IEEE Access, 5 (2017) 5247-5261.
[11] D.C. Mocanu, E. Mocanu, P.H. Nguyen, M. Gibescu, A. Liotta, Big IoT data mining for real-time energy disaggregation in buildings, in: Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on, IEEE, 2016, pp. 003765-003769.
[12] L. Wald, Some terms of reference in data fusion, IEEE Transactions on geoscience and remote sensing, 37(3) (1999) 1190-1193.
[13] E.F. Nakamura, A.A. Loureiro, A.C. Frery, Information fusion for wireless sensor networks: Methods, models, and classifications, ACM Computing Surveys (CSUR), 39(3) (2007) 9.
[14] H. Almuallim, T.G. Dietterich, Learning With Many Irrelevant Features, in: AAAI, 1991, pp. 547-552.
[15] Y. Sun, D. Wu, A relief based feature extraction algorithm, in: Proceedings of the 2008 SIAM International Conference on Data Mining, SIAM, 2008, pp. 188-195.
[16] M.S. Mohamad, Feature selection method using genetic algorithm for the classification of small and high dimension data, in: Proc. Int. Symp. Info. Com. Tech., 2004, 2004, pp. 13-16.
[17] A. Golmohammadi, N. Shams Ghareneh, A. Keramati, B. Jahandideh, Importance analysis of travel attributes using a rough set-based neural network: The case of Iranian tourism industry, Journal of Hospitality and Tourism Technology, 2(2) (2011) 155-171.
[18] B. Ahn, S. Cho, C. Kim, The integrated methodology of rough set theory and artificial neural network for business failure prediction, Expert systems with applications, 18(2) (2000) 65-74.
[19] G.H. John, R. Kohavi, K. Pfleger, Irrelevant features and the subset selection problem, in: Machine learning: proceedings of the eleventh international conference, 1994, pp. 121-129.
[20] S. Yang, J. Gu, Feature selection based on mutual information and redundancy-synergy coefficient, Journal of Zhejiang University-Science A, 5(11) (2004) 1382-1391.
[21] D. Wei, Clustering algorithms for sensor networks and mobile ad hoc networks to improve energy efficiency, University of Cape Town, 2007.
[22] Y. LiCF, W. ChenGH, An Energy-Efficient Unequal Clustering Mechanism for Wireless Sensor Networks, Proceedings of the Second IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS2005), Washing ton, DC, (2005).
[23] L. Fausett, L. Fausett, Fundamentals of neural networks: architectures, algorithms, and applications, Prentice- Hall, 1994.
[24] J. Langeveld, A.P. Engelbrecht, A generic set-based particle swarm optimization algorithm, in: International conference on swarm intelligence, ICSI, 2011, pp. 1-10.
[25] http://web.mit.edu/cron/group/house_n/data/PlaceLab/ PlaceLab.htm [seen Aug., 2017]
[26] D. Roobaert, G. Karakoulas, N. Chawla, Information gain, correlation and support vector machines, Feature extraction, (2006) 463-470.
[27] L. Yu, H. Liu, Feature selection for high-dimensional data: A fast correlation-based filter solution, in: Proceedings of the 20th international conference on machine learning (ICML-03), 2003, pp. 856-863.