Improved SVM-Recursive Feature Elimination (ISVM-RFE) Based Feature Selection for Bigdata Classification Under Map Reduce Framework

Abstract

Big data is widely recognized for its methodical collection and analysis of massive, particularly complex datasets. But handling the speed of the irregularity of information in the massive datasets requires a dependable system, which is difficult to achieve with big data processing. This paper proposes a new big data classification under a map-reduce framework under Improved Support Vector Machine- Recursive Feature Elimination (SVM-RFE) based feature selection. At first, inconsistent data values are eliminated by preprocessing the dataset, in which the data normalization technique is employed. Then the pre-processed data is processed via a map-reduce framework to handle the bigdata, wherein the mapper phase, selects the features by the ISVM-RFE approach. The reducer phase merges all the features and selects the appropriate features. In the end, the hybrid classification model, which combines an enhanced LSTM and CNN, receives the chosen features. Particularly, the LSTM model is improved in its loss calculation, where the hybrid loss function is introduced containing inverse dice loss function and inverse binary cross entropy loss function. The improved score level fusion method, which uses this method to produce a double sigmoid normalization mechanism for enhanced classification, determines the final classification results.