@article {Mountassir201664,
	title = {New under-sampling methods to address the problem of unbalanced sentiment classification: Application on Arabic datasets},
	journal = {International Journal of Information and Communication Technology},
	volume = {9},
	number = {1},
	year = {2016},
	note = {cited By 0},
	pages = {64-77},
	abstract = {This paper presents the study we have carried out to address the problem of unbalanced datasets in supervised sentiment classification in an Arabic context. We propose three different methods to under-sample the majority class documents. Our goal is to compare the effectiveness of the proposed methods with the common random under-sampling. We also aim to evaluate the behaviour of the classifier toward different under-sampling rates. We use three different common classifiers, namely Na{\"\i}ve Bayes, support vector machines and k-nearest neighbours. The experiments are carried out on two different Arabic datasets that we have built internally. We show that results obtained on the first dataset, which is slightly skewed, are better than those obtained on the second one which is highly skewed. We conclude also that Na{\"\i}ve Bayes is sensitive to dataset size, the more we reduce the data the more the results degrade. However, support vector machines are highly sensitive to unbalanced datasets. We record an instable behaviour of k-nearest neighbour. The results show also that we can rely on the proposed techniques and that they are typically competitive with random under-sampling. {\textcopyright} 2016 Inderscience Enterprises Ltd.},
	doi = {10.1504/IJICT.2016.077687},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979641121\&doi=10.1504\%2fIJICT.2016.077687\&partnerID=40\&md5=3261431f79a2a1c6ae94fa16e8ad8b01},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@article {Mountassir2014442,
	title = {The nearest centroid based on vector norms: A new classification algorithm for a new document representation model},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8556 LNAI},
	year = {2014},
	note = {cited By 1},
	pages = {442-456},
	abstract = {In this paper, we present a novel model that we propose for document representation. In contrast with the classical Vector Space Model which represents each document by a unique vector in the feature space, our model consists in representing each document by a vector in the space of training documents of each category. We develop, for this novel model, a discriminative classifier which is based on the norms of the generated vectors by our model. We call this algorithm the Nearest Cetroid based on Vector Norms. Our major goal, by the proposition of such new classification framework, is to overcome the problems related to huge dimensionality and vector sparsity which are commonly faced in Text Classification problems. We evaluate the performance of the proposed framework by comparing its effectiveness and efficiency with those of some standard classifiers when used with the classical document representation. The studied classifiers are Na{\"\i}ve Bayes (NB), Support Vector Machines (SVM) and k-Nearest Neighbors (kNN). We conduct our experiments on multi-lingual balanced and unbalanced binary data sets. Our results show that our algorithm typically performs well since it is competitive with the classical methods and, at the same time, dramatically faster especially in comparison with NB and kNN. We also apply our model on the Reuters21578 corpus so as to evaluate its performance in a multi-class environment. We can say that the obtained result (85.4\% in terms of micro-F1) is promising and that it can be improved in future works. {\textcopyright} 2014 Springer International Publishing Switzerland.},
	doi = {10.1007/978-3-319-08979-9_34},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958526350\&doi=10.1007\%2f978-3-319-08979-9_34\&partnerID=40\&md5=a5d3d5cd03b740999c7e4bcf28e33e1a},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@article {ElBakkali2010203,
	title = {A new task engineering approach for workflow access control},
	journal = {Advances in Intelligent and Soft Computing},
	volume = {85},
	year = {2010},
	note = {cited By 1},
	pages = {203-212},
	doi = {10.1007/978-3-642-16626-6_22},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80053005691\&doi=10.1007\%2f978-3-642-16626-6_22\&partnerID=40\&md5=b9d1f4d19bd722e72574f0bef90c7cff},
	author = {El Bakkali, H. and Hatim, H. and Berrada, I.}
}
@article {Elmeziane2008113,
	title = {A new artificial immune system for the detection of abnormal behaviour},
	journal = {Studies in Computational Intelligence},
	volume = {149},
	year = {2008},
	note = {cited By 1},
	pages = {113-122},
	doi = {10.1007/978-3-540-70560-4_10},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-52049089233\&doi=10.1007\%2f978-3-540-70560-4_10\&partnerID=40\&md5=d907d2de93d5249a4ec8aa418b36c4a9},
	author = {Elmeziane, R. and Berrada, I. and Kassou, I.}
}
