Abdelatif Ennaji

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This paper presents a multi-classifier system design controlled by the topology of the learning data. Our work also introduces a training algorithm for an incremental self-organizing map (SOM). This SOM is used to distribute classification tasks to a set of classifiers. Thus, the useful classifiers are activated when new data arrives. Comparative results(More)
Text categorization is the process of classifying documents into a predefined set of categories based on its contents of keywords. Text classification is an extended type of text categorization where the text is further categorized into sub-categories. Many algorithms have been proposed and implemented to solve the problem of English text categorization and(More)
Semi-supervised learning is a machine learning paradigm in which induced hypothesis is improved by taking advantage of unlabeled data. In fact, Learning from unlabeled data provides innumerable advantages to a wide range of applications where there is a huge amount of unlabeled data freely available. It is particularly useful when labeled data is scarce.(More)
Documents indexing is the main step in a conventional document classification or information retrieval framework. This study aims to highlight the influence of features' type on the efficiency of a classification system. Empirical results on Arabic dataset reveal that the choice of extracted feature's type has a significant impact on conserving semantic(More)
—Ensemble learning is the machine learning paradigm concerned with utilizing multiple base classifiers which trained and then combined to achieve a strong generalization. This technique can be beneficial to semi-supervised learning, which exploits unlabeled data in addition to the labeled data, to achieve the best possible classification performance. One of(More)
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