Study of Double SMO Algorithm Based on Attributes Reduction
To solve the classification problem in data mining, this paper proposes double SMO algorithm based on attributes reduction. Firstly attributes reduction deletes irrelevant attributes (or dimensions) to reduce data amount, consequently the total calculation is reduced, the training speed is fastened and Classification mode is easy to understand. Secondly applying SMO algorithm on the sampling dataset to get the approximate separating hyperplane, and then we obtain all the support vectors of original dataset. Finally again use SMO algorithm on the support vectors to get the final separating hyperplane. It is shown in the experiments that the algorithm reduces the memory space, effectively avoids the noise points’ effect on the final separating hyperplane and the precision of the algorithm is better than Decision Tree, Bayesian and Neural Network.
KeywordsData mining Support vector machine Attribute reduction Training algorithm
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