Title: Hybrid classifier model for big data by leveraging map reduce framework

Authors: V. Sitharamulu; K. Rajendra Prasad; K. Sudheer Reddy; A.V. Krishna Prasad; M. Venkat Dass

Addresses: Department of Computer Science and Engineering, GITAM School of Technology, GITAM (Deemed-to-be-University), Hyderabad, India ' Department of CSE, Institute of Aeronautical Engineering, Hyderabad, India ' Department of Information Technology, Anurag University, Hyderabad, India ' Department of Information Technology, MVSR Engineering College, Hyderabad, India ' College of Engineering, Osmania University, Hyderabad, India

Abstract: 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.

Keywords: big data classification; MapReduce framework; long short-term memory; LSTM; deep belief network; DBN; optimisation.

DOI: 10.1504/IJDMMM.2024.136219

International Journal of Data Mining, Modelling and Management, 2024 Vol.16 No.1, pp.23 - 48

Received: 14 Dec 2022
Accepted: 07 May 2023
Published online: 22 Jan 2024 *