LARODEC

Bardo, Tunisia
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Ben Amor N.,LARODEC | Essghaier F.,LARODEC | Fargier H.,IRIT
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017

This paper raises the question of solving multi-criteria sequential decision problems under uncertainty. It proposes to extend to possibilistic decision trees the decision rules presented in [1] for non sequential problems. It present a series of algorithms for this new framework: Dynamic Programming can be used and provide an optimal strategy for rules that satisfy the property of monotonicity. There is no guarantee of optimality for those that do not—hence the definition of dedicated algorithms. This paper concludes by an empirical comparison of the algorithms. © Springer International Publishing AG 2017.


Ben Amor N.,LARODEC | Khalfi Z.E.L.,LARODEC | Fargier H.,IRIT | Sabaddin R.,French National Institute for Agricultural Research
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2017

Possibilistic Markov Decision Processes offer a compact and tractable way to represent and solve problems of sequential decision under qualitative uncertainty. Even though appealing for its ability to handle qualitative problems, this model suffers from the drowning effect that is inherent to possibilistic decision theory. The present paper proposes to escape the drowning effect by extending to stationary possibilistic MDPs the lexicographic preference relations defined in [6] for nonsequential decision problems and provides a value iteration algorithm to compute policies that are optimal for these new criteria. © Springer International Publishing AG 2017.


Ben Amor N.,LARODEC | Dubois D.,French National Center for Scientific Research | Gouider H.,LARODEC | Prade H.,French National Center for Scientific Research
Information Sciences | Year: 2017

This paper studies the use of product-based possibilistic networks for representing preferences in multidimensional decision problems. This approach uses symbolic possibility weights and defines a partial preference order among solutions to a set of conditional preference statements on the domains of discrete decision variables. In the case of Boolean decision variables, this partial ordering is shown to be consistent with the preference ordering induced by the ceteris paribus assumption adopted in CP-nets. Namely, by completing the possibilistic net ordering with suitable constraints between products of symbolic weights, all CP-net preferences can be recovered. Computing procedures for comparing solutions are provided. The flexibility and representational power of the approach is stressed. © 2017 Elsevier Inc.


Cherichi S.,LARODEC | Faiz R.,LARODEC
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

Social mediating technologies have engendered radically new ways of information and communication, particularly during events; in case of natural disaster like earthquakes tsunami and American presidential election. The growing complexity of these social mediating technologies in terms of size, number of users, and variety of bloggers relationships have generated a big data which requires innovative approaches in order to analyse, extract and detect non-obvious and popular events. This paper is based on data obtained from Twitter because of its popularity and sheer data volume. This content can be combined and processed to detect events, entities and popular moods to feed various new large-scale data-analysis applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. Taking to account all the difficulties, we propose a new event detection approach combining linguistic features and Twitter features. Finally, we present our event detection system from microblogs that aims (1) detect new events, (2) to recognize temporal markers pattern of an event, (3) and to classify important events according to thematic pertinence, author pertinence and tweet volume. © Springer International Publishing Switzerland 2016.


Elkhlifi A.,Paris-Sorbonne University | Faiz R.,LARODEC
Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23 | Year: 2010

Event extraction is a significant task in information extraction. This importance increases more and more with the explosion of textual data available on the Web, the appearance of Web 2.0 and the tendency towards the Semantic Web. Thus, we propose a generic approach to extract events from text and to analyze them. We propose an event extraction algorithm with a polynomial complexity O(n5), and a new similarity measurement between events. We use this measurement to gather similar events. We also present a semantic map of events, and we validate the first component of our approach by the development of the "EventEC" system. Copyright © 2010, American Association for Artificial Intelligence (www.aaai.org ). All rights reserved.


Tlili T.,LARODEC | Krichen S.,LARODEC
Theoretical Computer Science | Year: 2015

We consider in this paper a double loading problem (DLP), an NP-hard optimization problem of extreme economic relevance in industrial areas. The problem consists in loading items into bins, then stowing bins in a set of compartments. The main objective is to minimize the number of used bins. We state the mathematical model as well as a modified binary particle swarm optimization with FFD initialization that outperforms state-of-the-art approaches carried out on a large testbed instances. © 2015 Elsevier B.V.


Benferhat S.,University of Artois | Smaoui S.,LARODEC
Fuzzy Sets and Systems | Year: 2011

Many algorithms deal with non-experimental data in possibilistic networks. Most of them are direct adaptations of the probabilistic approaches. In this paper, we propose to represent another kind of data which is experimental data caused by external interventions in possibilistic networks. In particular, we present different and equivalent graphical interpretations of such manipulations using an adaptation of the 'do' operator to a possibilistic framework. We then propose an efficient algorithm to evaluate effects of non-simultaneous sequences of both experimental and non-experimental data. The main advantage of our algorithm is that it unifies treatments of the two kinds of data through the conditioning process with only a small extra-cost. © 2010 Published by Elsevier B.V. All rights reserved.


Jemal D.,LARODEC | Faiz R.,LARODEC
CEUR Workshop Proceedings | Year: 2015

With the data volume that does not stop growing and the multitude of sources that led to diversity of structures, data processing needs are changing. Although, relational DBMSs remain the main data management technology for processing structured data, but faced with the massive growth in the volume of data, despite their evolution, relational databases, which have been proven for over 40 years, have reached their limits. Several organizations and researchers turned to MapReduce framework that has found great success in analyzing and processing large amounts of data on large clusters. In this paper, we will discuss MapReduce and Relational Database Management Systems as competing paradigms, and then as completing paradigms where we propose an integration approach to optimize OLAP queries process.


Bouchlaghem R.,LARODEC | Elkhelifi A.,Paris Sorbone University | Faiz R.,LARODEC
Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA | Year: 2016

We propose a machine learning approach for automatically classifying opinions of Twitter texts written in Modern Standard Arabic (MSA). Tweets are classified as either positive, negative, neutral or non-opinion. Various features for opinion classification have been used which are mainly linguistic and numeric. Our in-house collected and developed training data consists of tweets preserving their specifications such as @usermentions, #hashtags which are used as tweet-particular features. Four machine learning algorithms were applied on our dataset: Support Vector Machine (SVM), Naive Bayes (NB), J48 decision tree and Random forest. The experiments results show that SVM gives the highest F measure (72%), while the j48 classifier gives the highest precision (70,97%). Our experimental results demonstrate that tweet's specific features can significantly improve classification performance in comparison to other features combination. © 2015 IEEE.


Elkhlifi A.,Paris-Sorbonne University | Faiz R.,LARODEC
2010 ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2010 | Year: 2010

Event extraction is a significant task in information extraction. This importance increases more and more with the explosion of textual data available on the Web, the appearance of Web 2.0 and the tendency towards the Semantic Web. Thus, we propose a generic approach to extract events from text and to analyze them. We propose an event extraction algorithm with a polynomial complexity O(n5), and a new similarity measurement between events. We use this measurement to gather similar events. We also present a semantic map of events, and we validate the first component of our approach by the development of the "EventEC" system.

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