smart.lab.tunisia@gmail.com +216 71 588 443
Connexion

Big Data

Description

Aucune description disponible pour cet axe de recherche.

Membres

Hatem Ben Sta Hatem Ben Sta
Besma Ben Amara Besma Ben Amara
Ahmed Yosreddin Samti Ahmed Yosreddin Samti
Hamdi Ouechtati Hamdi Ouechtati
Mohamed Laabidi Mohamed Laabidi
Salah Ghodhbani Salah Ghodhbani

Publications

  • 2023
    Marwa Chabbouh, Slim Bechikh, Lamjed Ben Said, Efrén Mezura-Montes

    Imbalanced multi-label data classification as a bi-level optimization problem: application to miRNA-related diseases diagnosis

    Neural Comput. Appl. 35(22): 16285-16303 (2023), 2023

    Résumé

    In multi-label classification, each instance could be assigned multiple labels at the same time. In such a situation, the relationships between labels and the class imbalance are two serious issues that should be addressed. Despite the important number of existing multi-label classification methods, the widespread class imbalance among labels has not been adequately addressed. Two main issues should be solved to come up with an effective classifier for imbalanced multi-label data. On the one hand, the imbalance could occur between labels and/or within a label. The “Between-labels imbalance” occurs where the imbalance is between labels however the “Within-label imbalance” occurs where the imbalance is in the label itself and it could occur across multiple labels. On the other hand, the labels’ processing order heavily influences the quality of a multi-label classifier. To deal with these challenges, we propose in this paper a bi-level evolutionary approach for the optimized induction of multivariate decision trees, where the upper-level role is to design the classifiers while the lower-level approximates the optimal labels’ ordering for each classifier. Our proposed method, named BIMLC-GA (Bi-level Imbalanced Multi-Label Classification Genetic Algorithm), is compared to several state-of-the-art methods across a variety of imbalanced multi-label data sets from several application fields and then applied on the miRNA-related diseases case study. The statistical analysis of the obtained results shows the merits of our proposal.

  • Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled Ghedira

    A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Drift

    book-chapter in learning from data streams in evolving environments, pp 39-61. Springer International Publishing, January 2019., 2019

    Résumé

    Recent advances in Computational Intelligent Systems have focused on addressing complex problems related to the dynamicity of the environments. Generally in dynamic environments, data are presented as streams that may evolve over time and this is known by concept drift. Handling concept drift through ensemble classifiers has received a great interest in last decades. The success of these ensemble methods relies on their diversity. Accordingly, various diversity techniques can be used like block-based data, weighting-data or filtering-data. Each of these diversity techniques is efficient to handle certain characteristics of drift. However, when the drift is complex, they fail to efficiently handle it. Complex drifts may present a mixture of several characteristics (speed, severity, influence zones in the feature space, etc.) which may vary over time. In this case, drift handling is more complicated and requires new detection and updating tools. For this purpose, a new ensemble approach, namely EnsembleEDIST2, is presented. It combines the three diversity techniques in order to take benefit from their advantages and outperform their limits. Additionally, it makes use of EDIST2, as drift detection mechanism, in order to monitor the ensemble’s performance and detect changes. EnsembleEDIST2 was tested through different scenarios of complex drift generated from synthetic and real datasets. This diversity combination allows EnsembleEDIST2 to outperform similar ensemble approaches in terms of accuracy rate, and present stable behaviors in handling different scenarios of complex drift.

  • Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled Ghedira

    Self-Adaptive Windowing Approach for Handling Complex Concept Drift

    Cognitive Computation Journal, Springer. vol.7, pages 772–790, issue.6 (2015), Evolving Systems, Springer-Verlag Berlin Heidelberg 2016, 2015

    Résumé

    Detecting changes in data streams attracts major attention in cognitive computing systems. The challenging issue is how to monitor and detect these changes in order to preserve the model performance during complex drifts. By complex drift, we mean a drift that presents many characteristics in the sometime. The most challenging complex drifts are gradual continuous drifts, where changes are only noticed during a long time period. Moreover, these gradual drifts may also be local, in the sense that they may affect a little amount of data, and thus make the drift detection more complicated. For this purpose, a new drift detection mechanism, EDIST2, is proposed in order to deal with these complex drifts. EDIST2 monitors the learner performance through a self-adaptive window that is autonomously adjusted through a statistical hypothesis test. This statistical test provides theoretical guarantees, regarding the false alarm rate, which were experimentally confirmed. EDIST2 has been tested through six synthetic datasets presenting different kinds of complex drift, and five real-world datasets. Encouraging results were found, comparing to similar approaches, where EDIST2 has achieved good accuracy rate in synthetic and real-world datasets and has achieved minimum delay of detection and false alarm rate.

Projets