@conference {ElMrabtt2020,
	title = {Novel Convex Polyhedron Classifier for Sentiment Analysis},
	booktitle = {Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020},
	year = {2020},
	note = {cited By 1},
	abstract = {In this paper, we propose a Novel Convex Polyhedron classifier (NCPC) based on the geometric concept convex hull. NCPC is basically a linear piecewise classifier (LPC). It partitions linearly non-separable data into various linearly separable subsets. For each of these subset of data, a linear hyperplane is used to classify them. We evaluate the performance of this classifier by combining it with two feature selection methods (Chi-squared and Anova F-value). Using two datasets, the results indicate that our proposed classifier outperforms other LPC-based classifiers. {\textcopyright} 2020 IEEE.},
	keywords = {Artificial intelligence, Chi-Squared, Classification (of information), Cloud computing, Convex hull, Convex polyhedrons, F values, Feature selection methods, Geometric concepts, Geometry, Linearly separable, Piece-wise, Sentiment analysis},
	doi = {10.1109/CloudTech49835.2020.9365906},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102620927\&doi=10.1109\%2fCloudTech49835.2020.9365906\&partnerID=40\&md5=143fd1c523fe8ae24563a9b0482f632c},
	author = {El Mrabtt, S. and Lazaar, M. and Al Achhab, M. and Omara, H.}
}
