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
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 ...
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
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