A New Hybrid Multiclass Approach Based on KNN and SVM

Hela Limam, Amal Zouhair () and Wided Oueslati
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Hela Limam: Institut Supérieur d’Informatique, Université de Tunis El Manar, 2 Rue Abou Raihane Bayrouni, 2080 l’Ariana, Tunisia†Laboratoire BestMod, Institut Supérieur de Gestion de Tunis, University of Tunis, 41 Rue de la Liberté, Cité Bouchoucha, Le Bardo, 2000 Tunis, Tunisia
Amal Zouhair: ��Laboratoire BestMod, Institut Supérieur de Gestion de Tunis, University of Tunis, 41 Rue de la Liberté, Cité Bouchoucha, Le Bardo, 2000 Tunis, Tunisia
Wided Oueslati: ��Laboratoire BestMod, Institut Supérieur de Gestion de Tunis, University of Tunis, 41 Rue de la Liberté, Cité Bouchoucha, Le Bardo, 2000 Tunis, Tunisia‡Ecole Supérieure de Commerce de Tunis, University of Manouba, Campus Universitaire la Manouba, 2010 Manouba, Tunisia

Journal of Information & Knowledge Management (JIKM), 2022, vol. 21, issue 04, 1-16

Abstract: Support vector machine (SVM) is a machine learning method widely used in solving binary data classification problems due to its performance. Nevertheless, in practical problems of classification, there are often cases of the presence of more than two classes of objects in the original dataset. The paper considers a solution to the problem of SVM multiclass with the aim to increase the data classification quality based on a new way of hybridisation between SVM and k-nearest neighbour (KNN) algorithms. The first phase of the approach is called the filtering phase. At this level, the feature space is split into two classes separated by a hyperplane. In the next step called review, we generate a second hyperplane, then we calculate the distance between each test pattern and the second hyperplane in the feature space using e.g. the KNN function. The result of the two phases is three classes instead of two produced by the conventional SVM. For evaluation purposes, dataset experiments are conducted on seven benchmark datasets that have high dimensionality and large size. Numerical experiments show that the 3SVM approach can improve not only the accuracy compared to other multiclass SVM approaches, but also the precision, recall, and F1-score.

Keywords: KNN; SVM improvement; SVM multiclass; filtering; review; hybrid method (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219649222500617

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