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Meta-heuristic Algorithms for Text Feature Selection Problems

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Advances in Machine Intelligence and Computer Science Applications (ICMICSA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 656))

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Abstract

The increasing unstructured amount of text information on the Internet has become a significant obstacle to user needs. The text clustering approach is used in text mining to divide a group of texts into a predetermined number of clusters. The clustering algorithms suffer from the increasing amount of non-informative words in the corpus. Non-informative text features are removed from each document to improve clustering approach performance and computation. Here, several particle swarm optimization variants, such as inertia weight and constriction factor, are compared to improve the particle exploration experience. The PSO method is compared with the other commonly used metaheuristics (i.e., the Genetic algorithm and the Harmony search algorithm). Also, various exploration and initialization characteristics are integrated with the PSO to improves its performance. The experiments were carried out on four standard datasets: Reuters-21578, 20Newsgroups, Classic4, and WebKB. The experimental results show that PSO outperforms the other competing approaches in terms of clustering Accuracy, Precision, Recall, and F-measure. Moreover, adjusting PSO parameters improves its performance.

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Author information

Authors and Affiliations

  1. National School of Applied Sciences Khouribga, Sultan Moulay Slimane University, Beni Mellal, Morocco

    Issam Lakouam & Imad Hafidi

  2. Faculty of Sciences and Technologies, Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Mourad Nachaoui

Authors
  1. Issam Lakouam
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  2. Imad Hafidi
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  3. Mourad Nachaoui
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Corresponding author

Correspondence to Issam Lakouam .

Editor information

Editors and Affiliations

  1. National School of Applied Sciences of Khouribga, Sultan Moulay Slimane University, Khouribga, Morocco

    Noureddine Aboutabit

  2. ENSIAS, Mohammed V University, Rabat, Morocco

    Mohamed Lazaar

  3. National School of Applied Sciences of Khouribga, Sultan Moulay Slimane University, Khouribga, Morocco

    Imad Hafidi

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Lakouam, I., Hafidi, I., Nachaoui, M. (2023). Meta-heuristic Algorithms for Text Feature Selection Problems. In: Aboutabit, N., Lazaar, M., Hafidi, I. (eds) Advances in Machine Intelligence and Computer Science Applications. ICMICSA 2022. Lecture Notes in Networks and Systems, vol 656. Springer, Cham. https://doi.org/10.1007/978-3-031-29313-9_5

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