RETRACTED ARTICLE: An automated exploring and learning model for data prediction using balanced CA-SVM

Abstract

The rainfall prediction is important for metrological department as it closely associated with our environment and human life. An accuracy of rainfall prediction has great important for countries like India whose economy is dependent on agriculture. Because of dynamic nature of atmosphere, statistical techniques fail to predict rainfall information. The process of support vector machine (SVM) is to find an optimal boundary also known as hyper plane in which separates the samples (examples in a dataset) of different classes by a maximum margin. The proposed model uses the dynamic integrated model for exploring and learning large amount of data set. Balanced communication-avoiding support vector machine (CA-SVM) prediction model is proposed to achieve better performance and accuracy with limited number of iteration without any error. The rain fall dataset is used for performance evaluation. The proposed model starts with independent sample to the integrated samples without any collision in prediction. The proposed algorithm achieves 89% of accuracy when compared to the existing algorithms. The simulations demonstrate that prediction models indicate that the performance of the proposed algorithm Balanced CA-SVM has much better accuracy than the local learning model based on a set of experimental data if other things are equal. On the other hand, simulation results demonstrate the effectiveness and advantages of the Balanced CA-SVM model used in machine learning and further promises the scope for improvement as more and more relevant attributes can be used in predicting the dependent variables.