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

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 defaul...
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Autor*in:	Liliya A. Demidova [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	binary SVM classifier one-class SVM classifier kNN classifier hybrid classifier class imbalance problem

Übergeordnetes Werk:	In: Symmetry - MDPI AG, 2009, 13(2021), 4, p 615
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:4, p 615

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/sym13040615

Katalog-ID:	DOAJ030415918

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callnumber-first	Q - Science
author	Liliya A. Demidova
spellingShingle	Liliya A. Demidova misc QA1-939 misc binary SVM classifier misc one-class SVM classifier misc kNN classifier misc hybrid classifier misc class imbalance problem misc Mathematics Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms
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topic_title	QA1-939 Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms binary SVM classifier one-class SVM classifier kNN classifier hybrid classifier class imbalance problem
topic	misc QA1-939 misc binary SVM classifier misc one-class SVM classifier misc kNN classifier misc hybrid classifier misc class imbalance problem misc Mathematics
topic_unstemmed	misc QA1-939 misc binary SVM classifier misc one-class SVM classifier misc kNN classifier misc hybrid classifier misc class imbalance problem misc Mathematics
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title	Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms
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title_full	Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms
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title_sort	two-stage hybrid data classifiers based on svm and knn algorithms
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title_auth	Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms

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