Performance Comparison of Machine Learning Algorithms
ISSN
2349-5162
Cite This Article
"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
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.
Key Words
Machine Learning, Classification, Decision Tree, Naïve Bayes, EEG Eye State
Cite This Article
"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
Publication Details
Published Paper ID: JETIR1801121
Registration ID: 180140
Published In: Volume 5 | Issue 1 | Year January-2018
"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