Construction of Ensemble Square Classification Approaches in MIMO OFDM
R A Veer1, S Arulselvi2, B Karthik3
1R A Veer, Research Scholar, Department of Electronics and Communication Engineering, BIHER- Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2S Arulselvi, Associate Professor, Department of Electronics and Communication Engineering, BIHER- Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
3B Karthik, Associate Professor, Department of Electronics and Communication Engineering, BIHER- Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2039-2041 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7867068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There are many ways of achieving enhancements in the process of prediction or estimation to have confidence in the learning models while classifying the outcomes in the patterns of underlying data. One of the primary ways in the field of data mining is by designing a set of ensembles. Ensembles are the construction to have different classifiers to improve the accuracy of prediction. This approach was recommended to discover the patterns of connectivity of EVA dataset in MIMO OFDM. The ensemble square algorithms are namely AdaBoostM1, Attribute Selected Classifier, Bagging, Classification via Regression, and Random Committee executed in this research exertion and originate the superlative algorithm for generous superlative accurateness.
Keywords: EVA, MIMO, IoT , OFDM, and Bagging.
Scope of the Article: IoT