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Marwa Hammami

Informations générales

Marwa Hammami
Grade

Maître Assistant

Biographie courte

Marwa Hammami received the B.Sc. degree in computer science from the National School of Engineers of Carthage, University of Carthage, Tunisia, in 2012, the M.Sc. degree in computer science from the Faculty of Sciences of Tunis, University of Tunis El Manar, Tunisia, in 2014, and the Ph.D. degree in computer science with management from the Higher Institute of Management of Tunis in 2021.

Her current research interests include feature selection and construction, mono-objective, multi-objective, bi-level optimization, as well as machine learning and computer vision. She has published several papers in international journals and conferences, including Neural Computing and Applications and Memetic Computing.

Dr. Hammami also serves as a reviewer for several international journals such as IEEE Transactions on Evolutionary Computation, Swarm and Evolutionary Computation, and Applied Soft Computing Journal.

Équipes

Publications

  • 2020
    Marwa Hammami, Slim Bechikh, Chih-Cheng Hung, Mohamed Makhlouf, Lamjed Ben Said

    Feature Construction as a Bi-level Optimization Problem.

    Neural Computing and Applications, 32(1), 13783–13804, 2020

    Résumé

    Feature selection and construction are important preprocessing techniques in data mining. They allow not only dimensionality reduction but also classification accuracy and efficiency improvement. While feature selection consists in selecting a subset of relevant feature from the original feature set, feature construction corresponds to the generation of new high-level features, called constructed features, where each one of them is a combination of a subset of original features. Based on these definitions, feature construction could be seen as a bi-level optimization problem where the feature subset should be defined first and then the corresponding (near) optimal combination of the selected features should be found. Motivated by this observation, we propose, in this paper, a bi-level evolutionary approach for feature construction. The basic idea of our algorithm, named bi-level feature construction genetic algorithm (BFC-GA), is to evolve an upper-level population for the task of feature selection, while optimizing the feature combinations at the lower level by evolving a follower population. It is worth noting that for each upper-level individual (feature subset), a whole lower-level population is optimized to find the corresponding (near) optimal feature combination (constructed feature). In this way, BFC-GA would be able to output a set of optimized constructed features that could be very informative to the considered classifier. A detailed experimental study has been conducted on a set of commonly used datasets with varying dimensions. The statistical analysis of the obtained results shows the competitiveness and the outperformance of our bi-level feature construction approach with respect to many state-of-the-art algorithms.

    Marwa Hammami, Slim Bechikh, Mohamed Makhlouf, Chih Cheng-Hung, Lamjed Ben Said

    Class Dependent Feature Construction as a Bi-level Optimization Problem

    IEEE Congress on Evolutionary Computation. pp 1-8, 2020

    Résumé

    Feature selection and construction are important
    pre-processing techniques in data mining. They allow not only
    dimensionality reduction but also classification accuracy and
    efficiency improvement. While feature selection consists in selecting a subset of relevant features from the original feature
    set, feature construction corresponds to the generation of new
    high-level features, called constructed features, where each one
    of them is a combination of a subset of original features. However,
    different features can have different abilities to distinguish
    different classes. Therefore, it may be more difficult to construct
    a better discriminating feature when combining features that are
    relevant to different classes. Based on these definitions, feature
    construction could be seen as a BLOP (Bi-Level Optimization
    Problem) where the feature subset should be defined in the upper
    level and the feature construction is applied in the lower level
    by performing mutliple followers, each of which generates a set
    class dependent constructed features. In this paper, we propose a
    new bi-level evolutionary approach for feature construction called
    BCDFC that constructs multiple features which focuses on distinguishing one class from other classes using Genetic Programming
    (GP). A detailed experimental study has been conducted on six
    high-dimensional datasets. The statistical analysis of the obtained
    results shows the competitiveness and the outperformance of our
    bi-level feature construction approach with respect to many stateof-art algorithms.

    Marwa Hammami, Slim Bechikh, Chih Cheng-Hung, Lamjed Ben Said

    Class-Dependent Weighted Feature Selection as a Bi-Level Optimization Problem.

    International Conference on Neural Information Processing. pp 269-278., 2020

    Résumé

    Feature selection aims at selecting relevant features from the original
    feature set, but these features do not have the same degree of importance. This can
    be achieved by feature weighting, which is a method for quantifying the capability of features to discriminate instances from different classes. Multiple feature
    selection methods have shown that different feature subset can reduce the data
    dimensionality and maintain or even improve the classification accuracy. However, different features can have different abilities to distinguish instances of one
    class from the other classes, which makes the feature selection process a difficult task by finding the optimal feature subset weighting vectors for each class.
    Motivated by this observation, feature selection and feature weighting could be
    seen as a BLOP (Bi-Level Optimization Problem) where the feature selection is
    performed in the upper level, and the feature weighting is applied in the lower
    level by performing mutliple followers, each of which generates a set of weighting vectors for each class. Only the optimal feature subset weighting vector is
    retrieved for each class. In this paper, we propose a bi-level evolutionary approach for class-dependent feature selection and weighting using Genetic Algorithm (GA), called Bi-level Class-Dependent Weighted Feature Selection (BCDWFS). The basic idea of our BCDWFS is to exploit the bi-level model for performing upper level feature selection and lower level feature weighting with the
    aim of finding the optimal weighting vectors of a subset of features for each class.
    Our approach has been assessed on ten datasets and compared to three existing
    approaches, using three different classifiers for accuracy evaluation. Experimental results show that our proposed algorithm gives competitive and better results
    with respect to the state-of-the-art algorithms.

  • Marwa Hammami, Slim Bechikh, Chih Cheng-Hung, Lamjed Ben Said

    Weighted- Feature Construction as a Bi-level Optimization Problem

    IEEE Congress on Evolutionary Computation. pp 1604-161., 2019

    Résumé

    Feature selection and construction are important
    pre-processing techniques in machine learning and data mining.
    They may allow not only dimensionality reduction but also
    classifier accuracy and efficiency improvement. Feature selection
    aims at selecting relevant features from the original feature set,
    which could be less informative to achieve good performance.
    Feature construction may work well as it creates new highlevel features, but these features do not have the same degree
    of importance, which makes the use of weighted-features construction a very challenging topic. In this paper, we propose a
    bi-level evolutionary approach for efficient feature selection and
    simultaneous feature construction and feature weighting, called
    Bi-level Weighted-Features Construction (BWFC). The basic idea
    of our BWFC is to exploit the bi-level model for performing
    feature selection and weighted-features construction with the
    aim of finding an optimal subset of features combinations. Our
    approach has been assessed on six high-dimensional datasets and
    compared against three existing approaches, using three different
    classifiers for accuracy evaluation. Experimental results show
    that our proposed algorithm gives competitive and better results
    with respect to the state-of-the-art algorithms

  • Marwa Hammami, Slim Bechikh, Chih Cheng-Hung, Lamjed Ben Said

    A Multi-objective Hybrid Filter-Wrapper Evolutionary Approach for Feature Selection.

    Memetic Computing (IF: 5.9). 11(2), 193-208., 2018

    Résumé

    Feature selection is an important pre-processing data mining task, which can reduce the data dimensionality and improve not
    only the classification accuracy but also the classifier efficiency. Filters use statistical characteristics of the data as the evaluation
    measure rather than using a classification algorithm. On the contrary, the wrapper process is computationally expensive
    because the evaluation of every feature subset requires running the classifier on the datasets and computing the accuracy
    from the obtained confusion matrix. In order to solve this problem, we propose a hybrid tri-objective evolutionary algorithm
    that optimizes two filter objectives, namely the number of features and the mutual information, and one wrapper objective
    corresponding to the accuracy. Once the population is classified into different non-dominated fronts, only feature subsets
    belonging to the first (best) one are improved using the indicator-based multi-objective local search. Our proposed hybrid
    algorithm, named Filter-Wrapper-based Nondominated Sorting Genetic Algorithm-II, is compared against several multiobjective and single-objective feature selection algorithms on eighteen benchmark datasets having different dimensionalities.
    Experimental results show that our proposed algorithm gives competitive and better results with respect to existing algorithms.

    Marwa Hammami, Slim Bechikh, Chih Cheng-Hung, Lamjed Ben Said

    A Multi-objective Hybrid Filter-Wrapper Approach For Feature Construction On High-Dimensional Data Using GP.

    Proceedings of the IEEE Congress on Evolutionary Computation. pp 1-8, 2018

    Résumé

    Feature selection and construction are important
    pre-processing techniques in data mining. They may allow
    not only dimensionality reduction but also classifier accuracy
    and efficiency improvement. These two techniques are of great
    importance especially for the case of high-dimensional data.
    Feature construction for high-dimensional data is still a very
    challenging topic. This can be explained by the large search space
    of feature combinations, whose size is a function of the number of
    features. Recently, researchers have used Genetic Programming
    (GP) for feature construction and the obtained results were
    promising. Unfortunately, the wrapper evaluation of each feature
    subset, where a feature can be constructed by a combination
    of features, is computationally intensive since such evaluation
    requires running the classifier on the data sets. Motivated by
    this observation, we propose, in this paper, a hybrid multiobjective evolutionary approach for efficient feature construction
    and selection. Our approach uses two filter objectives and one
    wrapper objective corresponding to the accuracy. In fact, the
    whole population is evaluated using two filter objectives. However,
    only non-dominated (best) feature subsets are improved using an
    indicator-based local search that optimizes the three objectives
    simultaneously. Our approach has been assessed on six highdimensional datasets and compared with two existing prominent
    GP approaches, using three different classifiers for accuracy
    evaluation. Based on the obtained results, our approach is shown
    to provide competitive and better results compared with two
    competitor GP algorithms tested in this study