LARODEC Laboratory

Le Bardo, Tunisia

LARODEC Laboratory

Le Bardo, Tunisia
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Mechti S.,LARODEC Laboratory | Abbassi A.,University of Sfax | Belguith L.H.,University of Sfax | Faiz R.,LARODEC Laboratory
Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA | Year: 2017

In this paper, we focus on the detection of the Arabic learners' mother tongue. The proposed method is based on the automatic classification using some data statistically extracted from a source corpus. We present a hybrid method that combines surface analysis in texts with an automatic learning method. Unlike the few techniques found in the state of the art, the features selection phase allowed improving performances. Therefore, the obtained results outperformed those provided by the best methods used for Arabic native language detection. © 2016 IEEE.

Ben-Romdhane H.,LARODEC Laboratory | Alba E.,University of Malaga | Krichen S.,LARODEC Laboratory
Applied Intelligence | Year: 2015

This article addresses a new dynamic optimization problem (DOP) based on the Predator-Prey (PP) relationship in nature. Indeed, a PP system involves two adversary species where the predator’s objective is to hunt the prey while the prey’s objective is to escape from its predator. By analogy to dynamic optimization, a DOP can be seen as a predation between potential solution(s) in the search space, which represents the predator, and the moving optimum, as the prey. Therefore we define the dynamic predator-prey problem (DPP) whose objective is to keep track of the moving prey, so as to minimize the distance to the optimum. To solve this problem, a dynamic approach that continuously adapts to the changing environment is required. Accordingly, we propose a new evolutionary approach based on three main techniques for DOPs, namely: multi-population scheme, random immigrants, and memory of past solutions. This hybridization of methods aims at improving the evolutionary approaches ability to deal with DOPs and to restrain as much as possible their drawbacks. Our computational experiments show that the proposed approach achieves high performance for DPP and and competes with state of the art approaches. © 2015 Springer Science+Business Media New York

Ben-Romdhane H.,LARODEC Laboratory | Alba E.,University of Malaga | Krichen S.,LARODEC Laboratory
Soft Computing | Year: 2013

Dynamic optimization problems (DOPs) have attracted considerable attention due to the wide range of problems they can be applied to. Lots of efforts have been expended in modeling dynamic situations, proposing algorithms, and analyzing the results (too often in a visual way). Numeric performance measurements and their statistical validation have been however barely used in the literature. Most of works in DOPs report only the best-of-generation fitness, due to its simplicity of computation. Although this measure indicates the best algorithm in terms of fitness, it does not provide any details about the actual strength and weakness of each algorithm. In this article, we conduct a comparative study among algorithms of different search modes via several performance measures to demonstrate their relative advantages. We discuss the role of using different performance measures in drawing balanced conclusions about algorithms for DOPs. © 2013 Springer-Verlag Berlin Heidelberg.

Chebbah M.,LARODEC Laboratory | Chebbah M.,University of Rennes 1 | Martin A.,University of Rennes 1 | Ben Yaghlane B.,LARODEC Laboratory
Advances in Intelligent and Soft Computing | Year: 2012

In the theory of belief functions many combination rules are proposed in the purpose of merging and confronting several sources opinions. Some combination rules are used when sources are cognitively independent whereas others are specific to dependent sources. In this paper, we suggest a method to quantify sources degrees of dependence in order to choose the more appropriate combination rule. We used generated mass functions to test the proposed method. © 2012 Springer-Verlag.

Bounhas M.,LARODEC Laboratory | Bounhas M.,P.A. College | Ghasemi Hamed M.,French National Center for Scientific Research | Ghasemi Hamed M.,DTI Detector Trade International | And 4 more authors.
Fuzzy Sets and Systems | Year: 2014

In real-world problems, input data may be pervaded with uncertainty. In this paper, we investigate the behavior of naive possibilistic classifiers, as a counterpart to naive Bayesian ones, for dealing with classification tasks in the presence of uncertainty. For this purpose, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. Here the possibility distributions that are used are supposed to encode the family of Gaussian probabilistic distributions that are compatible with the considered dataset. We consider two types of uncertainty: (i) the uncertainty associated with the class in the training set, which is modeled by a possibility distribution over class labels, and (ii) the imprecision pervading attribute values in the testing set represented under the form of intervals for continuous data. Moreover, the approach takes into account the uncertainty about the estimation of the Gaussian distribution parameters due to the limited amount of data available. We first adapt the possibilistic classification model, previously proposed for the certain case, in order to accommodate the uncertainty about class labels. Then, we propose an algorithm based on the extension principle to deal with imprecise attribute values. The experiments reported show the interest of possibilistic classifiers for handling uncertainty in data. In particular, the probability-to-possibility transform-based classifier shows a robust behavior when dealing with imperfect data. © 2013 Elsevier B.V. Published by Elsevier B.V. All rights reserved.

Amor N.B.,LARODEC Laboratory | Dubois D.,French National Center for Scientific Research | Gouider H.,LARODEC Laboratory | Prade H.,French National Center for Scientific Research
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

Representing preferences into a compact structure has become an important research topic. Graphical models are of special interest. Indeed, they facilitate elicitation, exhibit some form of independence, and serve as a basis for solving optimization and dominance queries about choices. The expressiveness of the representation setting and the complexity of answering queries are then central issues for each approach. This paper proposes an extensive overview of the main graphical models for preference representation and provides a comparative survey by emphasizing their main characteristics. We also indicate possible transformations between some of these models. We contrast qualitative models such as CP-nets and TCP-nets with quantitative ones such as GAI networks, UCP-nets, and Marginal utility nets, and advocate π-Pref nets, recently introduced by the authors, as an interesting compromise between the two types of models. © Springer International Publishing Switzerland 2016.

Bounhas M.,LARODEC Laboratory | Bounhas M.,P.A. College | Prade H.,French National Center for Scientific Research | Richard G.,French National Center for Scientific Research
Communications in Computer and Information Science | Year: 2014

Analogical proportion-based classification methods have been introduced a few years ago. They look in the training set for suitable triples of examples that are in an analogical proportion with the item to be classified, on a maximal set of attributes. This can be viewed as a lazy classification technique since, like k-nn algorithms, there is no static model built from the set of examples. The amazing results (at least in terms of accuracy) that have been obtained from such techniques are not easy to justify from a theoretical viewpoint. In this paper, we show that there exists an alternative method to build analogical proportion-based learners by statically building a set of inference rules during a preliminary training step. This gives birth to a new classification algorithm that deals with pairs rather than with triples of examples. Experiments on classical benchmarks of the UC Irvine repository are reported, showing that we get comparable results. © Springer International Publishing Switzerland 2014.

Ben-Romdhane H.,LARODEC Laboratory | Ben Jouida S.,LARODEC Laboratory | Krichen S.,FSJEG de Jendouba
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

The online knapsack problem (OKP) is a generalized version of the 0-1 knapsack problem (0-1KP) to a setting in which the problem inputs are revealed over time. Whereas the 0-1KP involves the maximization of the value of the knapsack contents without exceeding its capacity, the OKP involves the following additional requirements: items are presented one at a time, their features are only revealed at their arrival, and an immediate and irrevocable decision on the current item is required before observing the next one. This problem is known to be non-approximable in its general case. Accordingly, we study a relaxed variant of the OKP in which items delay is allowed: we assume that the decision maker is allowed to retain the observed items until a given deadline before deciding definitively on them. The main objective in this problem is to load the best subset of items that maximizes the expected value of the knapsack without exceeding its capacity. We propose an online algorithm based on dynamic programming, that builds-up the solution in several stages. Our approach incorporates a decision rule that identifies the most desirable items at each stage, then places the fittest ones in the knapsack. Our experimental study shows that the proposed algorithm is able to approach the optimal solution by a small error margin. © Springer International Publishing Switzerland 2014.

Ishak M.B.,LINA Laboratories | Leary P.,LINA Laboratory | Amor N.B.,LARODEC Laboratory
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI | Year: 2015

Probabilistic relational models (PRMs) extend Bayesian networks (BNs) to a relational data mining context. Even though a panoply of works have focused, separately, on Bayesian networks and relational databases random generation, no work has been identified for PRMs on that track. This paper provides an algorithmic approach allowing to generate random PRMs from scratch to cover the absence of generation process. The proposed method allows to generate PRMs as well as synthetic relational data from a randomly generated relational schema and a random set of probabilistic dependencies. This can be of interest for machine learning researchers to evaluate their proposals in a common framework, as for databases designers to evaluate the effectiveness of the components of a database management system. © 2014 IEEE.

Bahri O.,LARODEC Laboratory | Bahri O.,French Institute for Research in Computer Science and Automation | Ben Amor N.,LARODEC Laboratory | Talbi E.-G.,French Institute for Research in Computer Science and Automation
Communications in Computer and Information Science | Year: 2016

The paper addresses the robustness of multi-objective optimization problems with fuzzy data, expressed via triangular fuzzy numbers. To this end, we introduced a new robustness approach able to deal with fuzziness in the multi-objective context. The proposed approach is composed of two main contributions: First, new concepts of β-robustness are proposed to analyze fuzziness propagation to the multiple objectives. Second, an extension of our previously proposed evolutionary algorithms is suggested for integrating robustness. These proposals are illustrated on a multi-objective vehicle routing problem with fuzzy customer demands. The experimental results on different instances show the efficiency of the proposed approach. © Springer International Publishing Switzerland 2016.

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