 Hybrid classifier model for big data by leveraging map reduce frameworkV. Sitharamulu, K. Rajendra Prasad, K. Sudheer Reddy, A.V. Krishna Prasad and M. Venkat Dass International Journal of Data Mining, Modelling and Management, 2024, vol. 16, issue 1, 23-48 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. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:16:y:2024:i:1:p:23-48 Access Statistics for this article More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.