Standardized Variable Distances: A distance-based machine learning method

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dc.authoridElen, Abdullah/0000-0003-1644-0476
dc.contributor.authorElen, Abdullah
dc.contributor.authorAvuclu, Emre
dc.date.accessioned2024-09-29T15:55:04Z
dc.date.available2024-09-29T15:55:04Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractToday, machine learning algorithms are an important research area capable of analyzing and modeling data in any field. Information obtained through machine learning methods helps researchers and planners to understand and review systematic problems of their current strategies. Thus, it is very important to work fully in every field that facilitates human life, such as early and correct diagnosis, correct choice, fully functioning autonomous systems. In this paper, a novel machine learning algorithm for multiclass classification is presented. The proposed method is designed based on the Minimum Distance Classifier (MDC) algorithm. The MDC is variance-insensitive because it classifies input vectors by calculating their distances/similarities with respect to class-centroids (average value of input vectors of a class). As it is known, real-world data contains certain proportions of noise. This situation negatively affects the performance of the MDC. To overcome this problem, we developed a variance-sensitive model, which we call Standardized Variable Distances (SVD), considering the standard deviation and z-score (standardized variable) factors. To ensure the accuracy of the SVD, we used Wisconsin Breast Cancer Original (WBCO) and LED Display Domain (led7digit) datasets, which we obtained from UCI machine learning repository, with 5-fold cross validation. It was compared and analyzed classification performance of the SVD with Decision Tree (DT), Random Forest (RF), k-Nearest Neighbor (k-NN), Multinomial Logistic Regression (MLR), Naive Bayes (NB), Support Vector Machine (SVM), and the Minimum Distance Classifier (MDC), which are well-known in the literature. It has also been compared thirteen different studies using the same datasets over the past five years. Our results in the experimental studies have shown that the SVD can classify better than traditional and state-of-the-art methods, compared in this study. The proposed method reached over 97% classification accuracy (CACC), F-measure (FM) and area under the curve (AUC) on the WBCO dataset. On the led7digit dataset, approximately 74% CACC, 75.1% FM and 82.2% AUC scores were obtained. It has been observed that the classification scores obtained with the SVD are higher than other ML algorithms used in the experimental studies. (C) 2020 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2020.106855
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85095567192en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2020.106855
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4428
dc.identifier.volume98en_US
dc.identifier.wosWOS:000603365800011en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectMulticlass classifieren_US
dc.subjectDistance-based classifieren_US
dc.titleStandardized Variable Distances: A distance-based machine learning methoden_US
dc.typeArticleen_US

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