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

Rihab Said

Informations générales

Rihab Said
Grade

Maître Assistant

Biographie courte

Rihab Said received the B.Sc., M.Sc., and Ph.D. degrees in Computer Science with Business from the University of Tunis, ISG-Tunis, Tunisia, in 2015, 2018, and 2022, respectively.

She is currently Assistant Professor with the University of Manouba, ENSI, Tunisia. Her current research interests include bi-level optimization, artificial intelligence, evolutionary machine learning, evolutionary computation, multi-objective optimization, metaheuristics, and their applications. She proposed several algorithms that were published in very renowned scientific journals and applied to several real-world problems.

Dr. Said has been selected among the TOP 20 WORLDWIDE in the international TOYP program for the year of 2024. This selection was based on her research and leadership careers.

She is a reviewer for several international journals such as IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, SWARM AND EVOLUTIONARY COMPUTATION, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, and IEEE ACCESS.

Équipes

Axes de recherche

Bi-level optimization
Artificial Intelligence
Evolutionary Computation
Evolutionary machine learning
Evolutionary optimization
Metaheuristics
Multi-Objective Optimization
Combinatorial Optimization

Publications

  • 2023
    Rihab Said, Slim Bechikh, Carlos A. Coello Coello, Lamjed Ben Said

    Solving the Discretization-based Feature Construction Problem using Bi-level Evolutionary Optimization

    2023 IEEE Congress on Evolutionary Computation (CEC), Chicago, IL, USA, 2023, pp. 1-8, 2023

    Résumé

    Feature construction represents a crucial data preprocessing technique in machine learning applications because it ensures the creation of new informative features from the original ones. This fact leads to the improvement of the classification performance and the reduction of the problem dimensionality. Since many feature construction methods require discrete data, it is important to perform discretization in order to transform the constructed features given in continuous values into their corresponding discrete versions. To deal with this situation, the aim of this paper is to jointly perform feature construction and feature discretization in a synchronous manner in order to benefit from the advantages of each process. Thus, we propose here to model the discretization-based feature construction task as a bi-level optimization problem in which the constructed features are evaluated based on their optimized sequence of cut-points. The resulting algorithm is termed Discretization-Based Feature Construction (Bi-DFC) where the proposed model is solved using an improved version of an existing co-evolutionary algorithm, named I-CEMBA that ensures the variation of concatenation trees. Bi-DFC performs the selection of original attributes at the upper level and ensures the creation and the evaluation of constructed features at the upper level based on their optimal corresponding sequence of cut-points. The obtained experimental results on ten high-dimensional datasets illustrate the ability of Bi-DFC in outperforming relevant state-of-the-art approaches in terms of classification results.

  • Rihab Said, Maha Elarbi, Slim Bechikh, Carlos Artemio Coello Coello, Lamjed Ben Said

    Interval-based Cost-sensitive Classification Tree Induction as a Bi-level Optimization Problem

    In 2022 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE., 2022

    Résumé

    Cost-sensitive learning is one of the most adopted approaches to deal with data imbalance in classification. Unfortunately, the manual definition of misclassification costs is still a very complicated task, especially with the lack of domain knowledge. To deal with the issue of costs' uncertainty, some researchers proposed the use of intervals instead of scalar values. This way, each cost would be delimited by two bounds. Nevertheless, the definition of these bounds remains as a very complicated and challenging task. Recently, some researches proposed the use of genetic programming to simultaneously build classification trees and search for optimal costs' bounds. As for any classification tree there is a whole search space of costs' bounds, we propose in this paper a bi-level evolutionary approach for interval-based cost-sensitive classification tree induction where the trees are constructed at the upper level while misclassification costs intervals bounds are optimized at the lower level. This ensures not only a precise evaluation of each tree but also an effective approximation of optimal costs intervals bounds. The performance and merits of our proposal are shown through a detailed comparative experimental study on commonly used imbalanced benchmark data sets with respect to several existing works.

    Rihab Said, Maha Elarbi, Slim Bechikh, Carlos Artemio Coello Coello

    Discretization-based feature selection as a bilevel optimization problem

    IEEE Transactions on Evolutionary Computation, 27(4), 893-907., 2022

    Résumé

    Discretization-based feature selection (DBFS) approaches have shown interesting results when using several metaheuristic algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), etc. However, these methods share the same shortcoming which consists in encoding the problem solution as a sequence of cut-points. From this cut-points vector, the decision of deleting or selecting any feature is induced. Indeed, the number of generated cut-points varies from one feature to another. Thus, the higher the number of cut-points, the higher the probability of selecting the considered feature; and vice versa. This fact leads to the deletion of possibly important features having a single or a low number of cut-points, such as the infection rate, the glycemia level, and the blood pressure. In order to solve the issue of the dependency relation between the feature selection (or removal) event and the number of its generated potential cut-points, we propose to model the DBFS task as a bilevel optimization problem and then solve it using an improved version of an existing co-evolutionary algorithm, named I-CEMBA. The latter ensures the variation of the number of features during the migration process in order to deal with the multimodality aspect. The resulting algorithm, termed bilevel discretization-based feature selection (Bi-DFS), performs selection at the upper level while discretization is done at the lower level. The experimental results on several high-dimensional datasets show that Bi-DFS outperforms relevant state-of-the-art methods in terms of classification accuracy, generalization ability, and feature selection bias.

    Rihab Said, Maha Elarbi, Slim Bechikh, Carlos Artemio Coello Coello

    Cost-sensitive classification tree induction as a bi-level optimization problem

    In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 284-287), 2022

    Résumé

    Data imbalance is still so far a challenging issue in data classification. In literature, cost-sensitive approach has been used to deal with such a challenge. Despite its interesting results, the manual design of cost matrices is still the main shortcoming of this approach. The data engineer is still facing a great difficulty in defining the misclassification costs, especially with the absence of domain specific knowledge. Recent works suggest the use of genetic programming as an effective tool to design classification trees with automatically learned costs. Although promising results were obtained, evaluating a classification tree with a single cost matrix is not a wise choice. Indeed, the tree quality evaluation requires trying several misclassification cost matrices to be more precise and fair. Motivated by this observation, we propose in this paper a bi-level modeling of the cost-sensitive classification tree induction problem where the upper level evolves the classification trees, while the cost matrix of each tree is optimized at the lower level. Our bi-level modeling is solved using an existing co-evolutionary algorithm, and the resulting method is named Bi-COS. The obtained comparative experimental results on several imbalanced benchmark datasets show the merits of Bi-COS with respect to the state-of-the art.

    Rihab Said, Maha Elarbi, Slim Bechikh, Lamjed Ben Said

    Solving combinatorial bi-level optimization problems using multiple populations and migration schemes

    Operational Research, 22(3), 1697-1735, 2022

    Résumé

    In many decision making cases, we may have a hierarchical situation between different optimization tasks. For instance, in production scheduling, the evaluation of the tasks assignment to a machine requires the determination of their optimal sequencing on this machine. Such situation is usually modeled as a Bi-Level Optimization Problem (BLOP). The latter consists in optimizing an upper-level (a leader) task, while having a lower-level (a follower) optimization task as a constraint. In this way, the evaluation of any upper-level solution requires finding its corresponding lower-level (near) optimal solution, which makes BLOP resolution very computationally costly. Evolutionary Algorithms (EAs) have proven their strength in solving BLOPs due to their insensitivity to the mathematical features of the objective functions such as non-linearity, non-differentiability, and high dimensionality. Moreover, EAs that are based on approximation techniques have proven their strength in solving BLOPs. Nevertheless, their application has been restricted to the continuous case as most approaches are based on approximating the lower-level optimum using classical mathematical programming and machine learning techniques. Motivated by this observation, we tackle in this paper the discrete case by proposing a Co-Evolutionary Migration-Based Algorithm, called CEMBA, that uses two populations in each level and a migration scheme; with the aim to considerably minimize the number of Function Evaluations (FEs) while ensuring good convergence towards the global optimum of the upper-level. CEMBA has been validated on a set of bi-level combinatorial production-distribution planning benchmark instances. The statistical analysis of the obtained results shows the effectiveness and efficiency of CEMBA when compared to existing state-of-the-art combinatorial bi-level EAs.

  • Rihab Said, Slim Bechikh, Ali Louati, Abdulaziz Aldaej, Lamjed Ben Said

    Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

    IEEE Access, vol. 8, pp. 141674-141695, 2020

    Résumé

    Many decision making situations are characterized by a hierarchical structure where a lower-level (follower) optimization problem appears as a constraint of the upper-level (leader) one. Such kind of situations is usually modeled as a BLOP (Bi-Level Optimization Problem). The resolution of the latter usually has a heavy computational cost because the evaluation of a single upper-level solution requires finding its corresponding (near) optimal lower-level one. When several objectives are optimized in each level, the BLOP becomes a multi-objective task and more computationally costly as the optimum corresponds to a whole non-dominated solution set, called the PF (Pareto Front). Despite the considerable number of recent works in multi-objective evolutionary bi-level optimization, the number of methods that could be applied to the combinatorial (discrete) case is much reduced. Motivated by this observation, we propose in this paper an Indicator-Based version of our recently proposed Co-Evolutionary Migration-Based Algorithm (CEMBA), that we name IB-CEMBA, to solve combinatorial multi-objective BLOPs. The indicator-based search choice is justified by two arguments. On the one hand, it allows selecting the solution having the maximal marginal contribution in terms of the performance indicator from the lower-level PF. On the other hand, it encourages both convergence and diversity at the upper-level. The comparative experimental study reveals the outperformance of IB-CEMBA on a multi-objective bi-level production-distribution problem. From the effectiveness viewpoint, the upper-level hyper-volume values and inverted generational distance ones vary in the intervals [0.8500, 0.9710] and [0.0072, 0.2420], respectively. From the efficiency viewpoint, IB-CEMBA has a good reduction rate of the Number of Function Evaluations (NFEs), lying in the interval [30.13%, 54.09%]. To further show the versatility of our algorithm, we have developed a case study in machine learning, and more specifically we have addressed the bi-level multi-objective feature construction problem.