A Novel Clustered Support Vector Machine with Reduced Support Vectors for Big Data Classification

Big data classification demands support vector models with huge number of support vectors, which are prone to overfitting and complex in nature. In this paper, we propose a method to solve the overfitting problem and improve the generalization of the model by reducing the number of support vectors. Using the proposed approach, the number of support vector has been reduced on average of 90% of the original count when trained using conventional SVM approach. The Discussed method proves to improve the performance of the conventional method by reducing the number of support vectors at cost of accuracy. This method can be used in applications involving long term prediction like weather/climate prediction and time critical applications which require rapid performance with a compensated accuracy.