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D. ASIR ANTONY GNANA SINGH1, S. APPAVU ALIAS BALAMURUGAN2, E. JEBAMALAR LEAVLINE3
- Department of Computer Science and Engineering, Anna University, BIT Campus, Tiruchirappalli – 620 024, Tamilnadu, India
- Department of Information Technology, K.L.N College of Information Technology, Sivagangai – 630 611, Tamilnadu, India
- Department of Electronics and Communication Engineering, Anna University, BIT Campus, Tiruchirappalli – 620 024, Tamilnadu, India
In the digital era, the application of image and signal classification influences several areas including medical, engineering, science, and technology. Due to the advancements in digital imaging and signal acquisition, images and signals are generated massively through various image and signal acquisition devices. Processing these massive images and signals for classification is a very challenging task to the researchers due to the high-dimensional space that contains irrelevant and redundant features. The irrelevant and redundant features reduce the performance of the classification algorithms in terms of classification accuracy. Therefore, the feature selection plays a significant role in the image and signal classification in order to reduce the irrelevant and redundant features from the high-dimensional space to improve the accuracy of the classifiers. This paper proposes a novel filtering approach with clustering based feature selection (FACFS) for image and signal classification. The performance of the proposed method is tested on various real-world image and signal datasets and compared with various state-of-the-art feature selection methods in terms of classification accuracy and redundancy rate. The experimental results show that the proposed method is very promising than the other methods compared..
Image and signal classification, Image acquisition devices, Digital imaging, Signal processing.
Submitted at: Dec. 4, 2015
Accepted at: Aug. 3, 2016
D. ASIR ANTONY GNANA SINGH, S. APPAVU ALIAS BALAMURUGAN, E. JEBAMALAR LEAVLINE, A novel filter based feature selection for image and signal classification, Journal of Optoelectronics and Advanced Materials Vol. 18, Iss. 7-8, pp. 645-653 (2016)
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