Hybrid classifier model for big data by leveraging map reduce framework

Big data technology is popular and desirable among many users for handling, analysing, and storing large data. However, clustering the large data has become more complex due to its size. In recent years, several techniques have been presented to retrieve the information from big data. The proposed hybrid classifier model CSDHAP, the hybridised form of sun flower optimisation (SFO) and deer hunting optimisation (DHO) algorithms with adaptive pollination rate using MapReduce framework. The CSDHAP is a data classification technique that performed using classifiers. The results of the presented approach are evaluated over the extant approaches using various metrics namely, F1-score, specificity, NPV, accuracy, FNR, FDR, sensitivity, precision, FPR, and MCC. It is pertinent to mention that, the proposed model is better than any of the traditional models. The proposed HC+CSDHAP model attained better precision value than other traditional models like RNN, SVM, CNN, Bi-LSTM, NB, LSTM, and DBN, correspondingly.