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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Honghui Yang Xin Zhou Yun Wang Jian Dai Sheng Shen Jingyu Liu |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of New Mexico, Albuquerque, NM, USA (Jingyu Liu) || Dept. of Environ. Eng., Northwestern Polytech. Univ., Xi'an, China (Honghui Yang; Xin Zhou; Yun Wang; Jian Dai; Sheng Shen) |
| Abstract | The performance of underwater acoustic target classification decreases and is unstable when the training set contains noisy, redundant or irrelevant samples. In this paper, a new adaptive immune clonal sample selection algorithm (AICISA) is proposed to address this problem. AICISA is aimed at directing generation evolution. An experiment about the application of AICISA using the multi-field features extracted from 4 kinds of underwater acoustic targets was conducted. Experimental results show that AICISA can select effective subsets of samples. Reducing the sample size by 90%, the classification accuracy of SVM is improved by 10%. AICISA also shows good convergence and stability. The optimal subset of samples obtained by AICISA has good generalization ability and can remarkably reduce the classification time. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 553117 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781479928255 |
| ISSN | 21593450 |
| e-ISBN | 9781479928279 |
| DOI | 10.1109/TENCON.2013.6718810 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-10-22 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Feature extraction Underwater acoustics Classification algorithms Signal processing algorithms Accuracy Support vector machines Cloning underwater acoustic target classification Adaptive immune clonal sample selection (AICISA) sample selection support vector machines (SVM) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering Computer Science Applications |
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