• Kasarapu Ramani

Eye state identification is generous of common time-series classification problem which is also a hot spot in recent research. The eye state classificationwidely usesElectroencephalography (EEG) to detect human’s cognition state.In this work, we investigated how the eye state (open or closed) can be predicted by evaluating brain waves with an EEG. Thus, we used Decision Tree and Naïve Bayes classification algorithms to develop a best model to classify the eye state as closed or open. Also the performance of these classification algorithms is tested on two different tools such as Weka and Spark. The performance is expressed in terms of parameters correctly classified instances, incorrectly classified instances, error rate and precision. The Decision Tree algorithm has outperformed with respect to Weka tool while the Naïve Bayes algorithm outperformed in the Spark.

Machine Learning, Classification, Decision Tree, Naïve Bayes, EEG Eye State

"Performance Comparison of Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 1, page no.635-638, January-2018, Available :http://www.jetir.org/papers/JETIR1801121.pdf

"Performance Comparison of Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 1, page no. pp635-638, January-2018, Available at : http://www.jetir.org/papers/JETIR1801121.pdf