AN ENHANCED CLASSIFICATION SYSTEM BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS AND DATA COMPLEXITY MEASURES
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Published:2023-05-30
Issue:2
Volume:23
Page:447-458
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ISSN:2068-3049
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Container-title:Journal of Science and Arts
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language:en
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Short-container-title:J. Sci. Arts
Author:
SAĞLAM FATIH1, DÜNDER EMRE1, CENGIZ MEHMET ALI1
Affiliation:
1. Ondokuz Mayıs University, Faculty of Sciences, Department of Statistics, 55420 Samsun, Turkey.
Abstract
Principal component analysis is commonly used as a pre-step before employing a classifier to avoid the negative effect of the dimensionality and multicollinearity. The performance of a classifier is severely affected by the deviations from the linearity of the data structure and noisy samples. In this paper, we propose a new classification system that overcomes the drawback of these crucial problems, simultaneously. Our proposal is relying on the kernel principal component analysis with a proper parameter selection approach with data complexity measures. According to the empirical results, F1, T2 and T3 in AUC, T3 in GMEAN and T2 and T3 in MCC performed better than classical and other complexity measures. Comparison of classifiers showed that Radial SVM performs better in AUC, and KNN performs better in GMEAN and MCC using KPCA with complexity measures. As a result, our proposed system produces better results in various classification algorithms with respect to classical approach.
Publisher
Valahia University of Targoviste - Journal of Science and Arts
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference29 articles.
1. Zhang, B.L., Australasian Joint Conference on Artificial Intelligence, Berlin, Heidelberg, pp. 719-728, 2006. 2. Garcia-Diaz, P., Sánchez-Berriel, I., Martínez-Rojas, J.A., Diez-Pascual, A.M., Genomics, 112(2), 1916, 2020. 3. Kelly, J.G., Angelov, P.P., Trevisan, J., Vlachopoulou, A., Paraskevaidis, E., Martin-Hirsch, P.L., Martin, F.L., Analytical and bioanalytical chemistry, 398(5), 2191, 2010. 4. Dess, B.W., Small, G.W., Journal of Environmental Radioactivity, 217, 1, 2020. 5. Kastorini, C.M., Papadakis, G., Milionis, H.J., Kalantzi, K., Puddu, P.E., Nikolaou, V., Vemmos, K.N., Goudevenos, J.A., Panagiotakos, D.B., Artificial intelligence in medicine, 59(3), 175, 2013.
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