Contribution to K-Nearest neighbour’s system for under water mine classification
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)
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This paper review mine detection and classification using high resolution images of the seafloor and under water mine by K-Nearest Neighbors algorithm the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for detection and classification In both cases, the input consists of the k closest training examples in the attribute space. The result depends on whether k-NN is used for detection or classification: e contributions concentrate on feature selection and object classification. the mine classification provide different method like Sophisticated Filter Method, Ensemble Learning, Automatic Target Recognition (ATR), Composite Relevance measure (CRM) , Semester-Shafer theory but these method are not clear classification in mine according to the object. Recent researchers are focus on the better classification according to the object information by K-Nearest Neighbors (K-NN).
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