Human action recognition based on boosted feature selection and naive Bayes nearest-neighbor classification

2013 ◽  
Vol 93 (6) ◽  
pp. 1521-1530 ◽  
Author(s):  
Li Liu ◽  
Ling Shao ◽  
Peter Rockett
Author(s):  
Ritam Guha ◽  
Ali Hussain Khan ◽  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Debotosh Bhattacharjee

Author(s):  
Hisao Ishibuchi ◽  
◽  
Tomoharu Nakashima

This paper proposes a genetic-algorithm-based approach for finding a compact reference set in nearest neighbor classification. The reference set is designed by selecting a small number of reference patterns from a large number of training patterns using a genetic algorithm. The genetic algorithm also removes unnecessary features. The reference set in our nearest neighbor classification consists of selected patterns with selected features. A binary string is used for representing the inclusion (or exclusion) of each pattern and feature in the reference set. Our goal is to minimize the number of selected patterns, to minimize the number of selected features, and to maximize the classification performance of the reference set. Computer simulations on commonly used data sets examine the effectiveness of our approach.


2013 ◽  
Vol 43 (4) ◽  
pp. 875-885 ◽  
Author(s):  
Qiuxia Wu ◽  
Zhiyong Wang ◽  
Feiqi Deng ◽  
Zheru Chi ◽  
David Dagan Feng

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