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Modified Support Vector Machine Algorithm to Reduce Misclassification and Optimizing Time Complexity
Aditya Ashvin Doshi, ,
Published in IGI Global
Pages: 34 - 56
A number of methodologies are available in the field of data mining, machine learning, and pattern recognition for solving classification problems. In past few years, retrieval and extraction of information from a large amount of data is growing rapidly. Classification is nothing but a stepwise process of prediction of responses using some existing data. Some of the existing prediction algorithms are support vector machine and k-nearest neighbor. But there is always some drawback of each algorithm depending upon the type of data. To reduce misclassification, a new methodology of support vector machine is introduced. Instead of having the hyperplane exactly in middle, the position of hyperplane is to be change per number of data points of class available near the hyperplane. To optimize the time consumption for computation of classification algorithm, some multi-core architecture is used to compute more than one independent module simultaneously. All this results in reduction in misclassification and faster computation of class for data point.
About the journal
JournalBig Data Analytics for Satellite Image Processing and Remote Sensing Advances in Computer and Electrical Engineering
PublisherIGI Global
Open Access0