NDLI: Study of feature selection for the stored-grain insects based on artificial immune algorithm

Content Provider	IEEE Xplore Digital Library
Author	Hongtao Zhang Yuxia Hu
Copyright Year	2010
Description	Author affiliation: School of Electric power, North China University of Water Conservancy and Electric Power, Zhengzhou, China (Hongtao Zhang; Yuxia Hu)
Abstract	The feature subset selection is critical for the real-time detection of the stored-grain insects based on image recognition technology. This study proposed a feature selection method based on artificial immune algorithm Immune (AIA). The single objective affinity evaluation function was developed to evaluate the feature subset by introducing the v-fold cross-validation training model accuracy and the number of the selected features. The feature subset selection for the nine species of the insects of wheat was conducted from the 17 morphological features by using AIA. Compared with the feature selection method based on genetic algorithm, AIA can further decrease the size of the feature subset and improve the classification accuracy. By using the 7 features selected based on AIA, the SVM classifier whose parameters had been optimized classified over 95.5% of the ninety image samples of the insects. The results show that it is practical and feasible.
Starting Page	140
Ending Page	142
File Size	547084
Page Count	3
File Format	PDF
ISBN	9781424451920
ISSN	19483422
DOI	10.1109/CAR.2010.5456751
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	support vector machine Insects stored-grain insects artificial immune algorithm feature selection
Content Type	Text
Resource Type	Article

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