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Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms

Symmetry (Basel), 2021-04, Vol.13 (4), p.615 [Peer Reviewed Journal]

2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2073-8994 ;EISSN: 2073-8994 ;DOI: 10.3390/sym13040615

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  • Title:
    Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms
  • Author: Demidova, Liliya A.
  • Subjects: Algorithms ; binary SVM classifier ; class imbalance problem ; Classification ; Classifiers ; Datasets ; Feasibility ; hybrid classifier ; kNN classifier ; one-class SVM classifier ; Optimization algorithms ; Parameters ; Strip ; Support vector machines ; Training
  • Is Part Of: Symmetry (Basel), 2021-04, Vol.13 (4), p.615
  • Description: The paper considers a solution to the problem of developing two-stage hybrid SVM-kNN classifiers with the aim to increase the data classification quality by refining the classification decisions near the class boundary defined by the SVM classifier. In the first stage, the SVM classifier with default parameters values is developed. Here, the training dataset is designed on the basis of the initial dataset. When developing the SVM classifier, a binary SVM algorithm or one-class SVM algorithm is used. Based on the results of the training of the SVM classifier, two variants of the training dataset are formed for the development of the kNN classifier: a variant that uses all objects from the original training dataset located inside the strip dividing the classes, and a variant that uses only those objects from the initial training dataset that are located inside the area containing all misclassified objects from the class dividing strip. In the second stage, the kNN classifier is developed using the new training dataset above-mentioned. The values of the parameters of the kNN classifier are determined during training to maximize the data classification quality. The data classification quality using the two-stage hybrid SVM-kNN classifier was assessed using various indicators on the test dataset. In the case of the improvement of the quality of classification near the class boundary defined by the SVM classifier using the kNN classifier, the two-stage hybrid SVM-kNN classifier is recommended for further use. The experimental results approve the feasibility of using two-stage hybrid SVM-kNN classifiers in the data classification problem. The experimental results obtained with the application of various datasets confirm the feasibility of using two-stage hybrid SVM-kNN classifiers in the data classification problem.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2073-8994
    EISSN: 2073-8994
    DOI: 10.3390/sym13040615
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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