Techniques Tanimoto correlated feature selection system and hybridization of clustering and boosting ensemble classification of remote sensed big data for weather forecasting

Abstract Weather forecasting has been done using various techniques but still not efficient for handling the big remote sensed data since the data comprises the more features. Hence the techniques degrade the forecasting accuracy and take more prediction time. To enhance the prediction accuracy (PA) with minimal time, Tanimoto Correlation based Combinatorial MAP Expected Clustering and Linear Program Boosting Classification (TC-CMECLPBC) Technique is proposed. At first, the data and features are gathered from big weather database. After that, relevant features are selected through finding the similarity between the features. Tanimoto Correlation Coefficient is used to find the similarity between the features for selecting the relevant features with higher feature selection accuracy. After selecting the relevant features, MAP expected clustering process is carried out to group the weather data for cluster formation. In this process, a number of cluster and cluster centroids are initialized. In this clustering process, it includes two steps namely expectation (E) and maximization (M) to discover maximum probability for grouping data into the cluster. After that, the clustering result is given to Linear Program boosting classifier to improve the prediction performance. In this classification, the weak classifier results are boosted to create strong classifier. The results evident that the TC-CMECLPBC technique enhance the PA with lesser time and false positive rate (FPR) than the conventional methods.