Evaluating Adaboost and Bagging Methods for Time Series Forecasting EEG Dataset
N. Geethanjali1, G.T. Prasanna Kumari2, M. Usha Rani3
1Dr. N. Geethanjali, Professor, Department of Computer Science, S.K. University, Ananthapur (Andhra Pradesh), India.
2G.T. Prasanna Kumari, Research Scholar, Department of Computer Science and Engineering, S V Engineering College for Women, Tirupati (Andhra Pradesh), India.
3Dr. M. Usha Rani, Professor, Department of Computer Science, SPMVV, Tirupati (Andhra Pradesh), India.
Manuscript received on 16 February 2019 | Revised Manuscript received on 07 March 2019 | Manuscript Published on 08 June 2019 | PP: 680-683 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E11400275S419/19©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: Time series forecasting is a paramount range from claiming machine learning that is frequently neglected. It is critical a direct result there are thus large portions prediction issues that include a period part. These issues are dismissed on account of it, this period part will lead to time series issues more troublesome to manage. An fascinating time series classification issue will be foreseeing if an subject’s eyes need aid open alternately shut based best for their brain wave information (EEG). We will aggravate examination for Adaboost and Bagging methodologies on EEG dataset.
Keywords: Adaboost, Bagging, EEG.
Scope of the Article: Probabilistic Models and Methods