@conference { ISI:000345642100034,
	title = {The Nearest Centroid Based on Vector Norms: A New Classification Algorithm for a New Document Representation Model},
	booktitle = {MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION, MLDM 2014},
	series = {Lecture Notes in Artificial Intelligence},
	volume = {8556},
	year = {2014},
	note = {10th International Conference on Machine Learning and Data Mining (MLDM), St Petersburg, RUSSIA, JUL 21-24, 2014},
	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 Naive 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.},
	isbn = {978-3-319-08979-9; 978-3-319-08978-2},
	issn = {0302-9743},
	author = {Mountassir, Asmaa and Benbrahim, Houda and Berrada, Ilham},
	editor = {Perner, P}
}
@conference { ISI:000316869203071,
	title = {An empirical study to address the problem of Unbalanced Data Sets in Sentiment Classification},
	booktitle = {PROCEEDINGS 2012 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)},
	series = {IEEE International Conference on Systems Man and Cybernetics Conference Proceedings},
	year = {2012},
	note = {IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, SOUTH KOREA, OCT 14-17, 2012},
	pages = {3298-3303},
	publisher = {IEEE Systems, Man, and Cybernetics Soc (SMC); IEEE; Korea Univ; Korean Soc Cognit Sci (KSCS); Korean Inst Informat Scientists and Engineers Soc Computat Intelligence (KSCI); Hi Seoul; Korea Tourism Org; Asian Off Aerosp Res and Dev (AOARD); Natl Res Fdn K},
	organization = {IEEE Systems, Man, and Cybernetics Soc (SMC); IEEE; Korea Univ; Korean Soc Cognit Sci (KSCS); Korean Inst Informat Scientists and Engineers Soc Computat Intelligence (KSCI); Hi Seoul; Korea Tourism Org; Asian Off Aerosp Res and Dev (AOARD); Natl Res Fdn K},
	abstract = {With the emergence of Web 2.0, Sentiment Analysis is receiving more and more attention. Several interesting works were performed to address different issues in Sentiment Analysis. Nevertheless, the problem of Unbalanced Data Sets was not enough tackled within this research area. This paper presents the study we have carried out to address the problem of unbalanced data sets in supervised sentiment classification in a multi-lingual context. We propose three different methods to under-sample the majority class documents. These methods are Remove Similar, Remove Farthest and Remove by Clustering. Our goal is to compare the effectiveness of the proposed methods with the common random under-sampling. We also aim to evaluate the behavior of the classifiers toward different under-sampling rates. We use three different common classifiers, namely Naive Bayes, Support Vector Machines and k-Nearest Neighbors. The experiments are carried out on two Arabic data sets and an English data set. We show that the four under-sampling methods are typically competitive. Naive Bayes is shown as insensitive to unbalanced data sets. But Support Vector Machines seems to be highly sensitive to unbalanced data sets; k-Nearest Neighbors shows a slight sensitivity to imbalance in comparison with Support Vector Machines.},
	isbn = {978-1-4673-1714-6},
	issn = {1062-922X},
	author = {Mountassir, Asmaa and Benbrahim, Houda and Berrada, Ilham}
}
@conference { ISI:000371495900008,
	title = {Some methods to address the problem of Unbalanced Sentiment Classification in an Arabic context},
	booktitle = {2012 COLLOQUIUM ON INFORMATION SCIENCE AND TECHNOLOGY (CIST{\textquoteright}12)},
	series = {Colloquium in Information Science and Technology},
	year = {2011},
	note = {IEEE Colloquium on Information Science and Technology (CIST), Fez, MOROCCO, OCT 22-24, 2012},
	pages = {43-48},
	publisher = {IEEE; IEEE Morocco Sect; IEEE Morocco Comp \& Commun Joint Chapter; USMBA IEEE Student Branch; Faculty of Sciences Dhar Mahraz; Faculty of Technical Sciences of Fez; IEEE Comp Soc; IEEE Commun Soc},
	organization = {IEEE; IEEE Morocco Sect; IEEE Morocco Comp \& Commun Joint Chapter; USMBA IEEE Student Branch; Faculty of Sciences Dhar Mahraz; Faculty of Technical Sciences of Fez; IEEE Comp Soc; IEEE Commun Soc},
	abstract = {The rise of social media (such as online web forums and social networking sites) has attracted interests to mining and analyzing opinions available on the web. The online opinion has become the object of studies in many research areas; especially that called {\textquoteleft}{\textquoteleft}Opinion Mining and Sentiment Analysis{{\textquoteright}{\textquoteright}}. Several interesting and advanced works were performed on few languages (in particular English). However, there were very few studies on some languages such as Arabic. This paper presents the study we have carried out to address the problem of unbalanced data sets 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 behavior of the classifier toward different under-sampling rates. We use two different common classifiers, namely Naive Bayes and Support Vector Machines. The experiments are carried out on an Arabic data set that we have built from Aljazeera{\textquoteright}s web site and labeled manually. The results show that Naive Bayes is sensitive to data set size, the more we reduce the data the more the results degrade. However, it is not sensitive to unbalanced data sets on the contrary of Support Vector Machines which is highly sensitive to unbalanced data sets. The results show also that we can rely on the proposed techniques and that they are typically competitive with random under-sampling.},
	isbn = {978-1-4673-2725-1},
	issn = {2327-185X},
	author = {Mountassir, Asmaa and Benbrahim, Houda and Berrada, Ilham},
	editor = {ElMohajir, M and Begdouri, A and ElMohajir, BE and Zarghili, A}
}
@conference { ISI:000289216200022,
	title = {A New Task Engineering Approach for Workflow Access Control},
	booktitle = {COMPUTATIONAL INTELLIGENCE IN SECURITY FOR INFORMATION SYSTEMS 2010},
	series = {Advances in Intelligent and Soft Computing},
	volume = {85},
	year = {2010},
	note = {3rd International Conference on Computational Intelligence in Security for Information Systems, Leon, SPAIN, NOV 11-12, 2010},
	pages = {203-212},
	publisher = {Junta Castilla Leon; Supercomputing Ctr Castilla Leon; Univ Leon; IEEE SECCION ESPANA; IEEE Syst Man \& Cybernet Spanish Chapter; MIR Lab; Int Fed Comp Logic},
	organization = {Junta Castilla Leon; Supercomputing Ctr Castilla Leon; Univ Leon; IEEE SECCION ESPANA; IEEE Syst Man \& Cybernet Spanish Chapter; MIR Lab; Int Fed Comp Logic},
	abstract = {Security and particularly Access control is a crucial issue for workflow management systems (WFMS). RBAC (Role based Access Control) model seems to be suitable for enforcing access control policies in such systems. However, without an effective role engineering process -at administration time- that aims to reduce conflicting situations. using RBAC could be more complex and less useful. Additionally to role engineering, a {\textquoteleft}task engineering{\textquoteright} process at run time- could be useful to allow the satisfaction of access control constraints in even critical situations. In this paper, we identify task engineering as a process to examine the granularity of each workflow{\textquoteright}s task in a way to meet at run time- the main workflow access control requirements, precisely the least privilege and separation of duties principles. Then, we propose an approach of task engineering to improve security enforcement in WFMS. This approach uses an intelligent method namely the Constraints Satisfaction Problem (CSP) formulation and resolution method.},
	isbn = {978-3-642-16625-9},
	issn = {1867-5662},
	author = {El Bakkali, Hanan and Hatim, Hamid and Berrada, Ilham},
	editor = {Herrero, A and Corchado, E and Redondo, C and Alonso, A}
}
@conference { ISI:000262046500010,
	title = {A New Artificial Immune System for the Detection of Abnormal Behaviour},
	booktitle = {SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING},
	series = {Studies in Computational Intelligence},
	volume = {149},
	year = {2008},
	note = {9th International Conference on Software Engineering Artificial Intelligence, Networking and Parallel/Distributed Computing, Phuket, THAILAND, AUG 06-08, 2008},
	pages = {113-122},
	publisher = {Int Assoc Comp \& Informat Sci; IEEE; IEEE Comp Soc; UniNet; Thailand Convent \& Exhibit Bureau},
	organization = {Int Assoc Comp \& Informat Sci; IEEE; IEEE Comp Soc; UniNet; Thailand Convent \& Exhibit Bureau},
	abstract = {We propose in this paper a new Artificial Immune System (AIS) named NK system, for the detection of abnormal behaviour with an unsupervised approach. Its originality resides in the unsupervised detection based on the mechanism of NK cell (Natural Killer cell) contrary to the existing AIS that use supervised approaches based on the mechanisms of the T and B cells. The NK cells develop the capacity to recognize the molecules of self-MHC through a unique class of receptors that can inhibit or activate its natural mechanism of the antigens elimination. In this paper, the NK system is applied to the detection of fraud in mobile phone. The experimental results axe very satisfactory instead of the very weak proportion of the fraudulent operations in our sample.},
	isbn = {978-3-540-70559-8},
	issn = {1860-949X},
	doi = {10.1007/978-3-540-70560-4\_10},
	author = {Elmeziane, Rachid and Berrada, Ilham and Kassou, Ismail},
	editor = {Lee, R}
}
