publication . Article . 2015

Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition

Chernbumroong, S.; Cang, Shuang; Yu, Hongnian;
Open Access
  • Published: 01 Jan 2015 Journal: Expert Systems with Applications, volume 42, pages 573-583 (issn: 0957-4174, Copyright policy)
  • Publisher: Elsevier BV
  • Country: United Kingdom
Abstract
We propose a feature selection algorithm using MRMC.Show that MRMC provides a good result comparing to the 3 popular algorithms.The complementary measure improves the performance of the Clamping algorithm.Evaluate the proposed algorithm on 2 well-defined problems and 5 real life data sets. In the multi-sensor activity recognition domain, the input space is often large and contains irrelevant and overlapped features. It is important to perform feature selection in order to select the smallest number of features which can describe the outputs. This paper proposes a new feature selection algorithms using the maximal relevance and maximal complementary (MRMC) based ...
Subjects
free text keywords: Data mining, computer.software_genre, computer, Feature selection, Machine learning, Data set, Minimum redundancy feature selection, Feature (computer vision), Computer science, Redundancy (engineering), Mutual information, Data type, Artificial intelligence, business.industry, business, Pattern recognition, Artificial neural network
Related Organizations

[10] M. M. Kabir, M. M. Islam, K. Murase, A new wrapper feature selection approach using neural network, Neurocomputing 73 (2010) 3273 - 3283.

[11] Y. Saeys, I. Inza, P. Larraaga, A review of feature selection techniques in bioinformatics, Bioinformatics 23 (2007) 2507-2517. [OpenAIRE]

[12] P. Estevez, M. Tesmer, C. Perez, J. Zurada, Normalized mutual information feature selection, Neural Networks, IEEE Transactions on 20 (2009) 189-201.

[13] J.-X. Peng, S. Ferguson, K. Rafferty, P. D. Kelly, An efficient feature selection method for mobile devices with application to activity recognition, Neurocomputing 74 (2011) 3543 - 3552.

[14] A. Dalton, G. Olaighin, Comparing supervised learning techniques on the task of physical activity recognition, Biomedical and Health Informatics, IEEE Journal of 17 (2013) 46-52.

multi-class support vector machines, Optimization Methods and Software 22 (2007) 225-236.

[26] A. Antos, B. K´egl, T. Linder, G. Lugosi, Data-dependent margin-based generalization bounds for classification, J. Mach. Learn. Res. 3 (2003) 73-98.

[27] K. P. Bennett, O. L. Mangasarian, Robust linear programming discrimination of two linearly inseparable sets, 1992.

[28] D. Ayres-de campos, J. Bernardes, A. Garrido, J. Marques-de s, L. Pereira-leite, Sisporto 2.0: A program for automated analysis of cardiotocograms, Journal of Maternal-Fetal and Neonatal Medicine 9 (2000) 311-318.

[29] B. Kijsirikul, S. Sinthupinyo, K. Chongkasemwongse, Approximate match of rules using backpropagation neural networks, Machine Learning 44 (2001) 273-299.

[30] I. Cohen, F. Cozman, N. Sebe, M. Cirelo, T. Huang, Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction, Pattern Analysis and Machine Intelligence, IEEE Transactions on 26 (2004) 1553-1566. [OpenAIRE]

Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue