Sammon Projected Feature Selection Based Linear Support Vector Regression for Big Data Predictive Analytics

Abstract

In big data analytics, classification is a key problem to be resolved for providing efficient prediction results. Recently, many research works have been designed for big data classification. However, accuracy was not increased with minimal time complexity when considering large dataset as input. In order to handle such issues, Sammon Projected Feature Selection based Linear Support Vector Regression (SPFS-LSVR) Method is introduced. The SPFS-LSVR method is developed to minimize the time and space complexity involved in the classification with better accuracy. At first, SPFS-LSVR method takes big dataset as input where it includes number of features and data for executing predictive analytics. Then theSammon Projection is employed in SPFS-LSVR methodfor performing the feature selection in high dimensional data. Sammon Projection helps in finding the relevant features by mapping the high-dimensional space to the lower-dimensionality space. This in turns, the time and space complexity occurred during the classification is reduced. After that, SPFS-LSVR method uses a Linear Support Vector Regression (LSVR) model for examining the selected features of input data with higher accuracy. The designed model uses hyperplane for producing the exact prediction result with higher accuracy. The LSVR model determines the relationship between independent data (i.e., features of input data) and the dependent data (i.e., prediction outcomes) with the help of Laplace kernel function. From that, the maximum relationship between the input features of data is classified into different classes. This helps in SPFS-LSVR method to enhance the performance of classification with maximum accuracy and minimal error rate. Through the efficient classification performance of big data, proposed SPFS-LSVR method improves the big data predictive analytics process as compared to state-of-the-art works. Experimental evaluation is carried out using big dataset on factors such as prediction time, prediction accuracy, false positive rate and space complexity with respect to number of data.