On Using Structural Patterns in Data for Classification

Arslan, Güvenç; Karabulut, Bergen; Ünver, Halil Murat

doi:10.17654/as065010033

Cited by 1 publication

(3 citation statements)

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“…In order to analyze the performance of the proposed classifiers, we considered 10 data sets that have also been used in some other studies (see, for example, [2,14,25]). We note that all data sets have numerical attributes only.…”

Section: Application To Real Data Sets and Performancementioning

confidence: 99%

“…In recent years, a new approach has been introduced: semi-supervised learning, which is a mixture of supervised learning and unsupervised learning. For semi-supervised learning, similaritybased approaches have also gained significant popularity in recent years (see, [1][2][3][4]).…”

Section: Introductionmentioning

confidence: 99%

“…Cheng et al, in [13], study local support vector machines for classification with nonlinear decision surfaces. Arslan et al proposed a new clustering algorithm to discover the structure of the data set and then used these clusters with SVM in [2]. Another recent study [14] used the CURE clustering algorithm with SVMs.…”

Section: Introductionmentioning

confidence: 99%

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An Algebraic Approach to Clustering and Classification with Support Vector Machines

2022

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In this note, we propose a novel classification approach by introducing a new clustering method, which is used as an intermediate step to discover the structure of a data set. The proposed clustering algorithm uses similarities and the concept of a clique to obtain clusters, which can be used with different strategies for classification. This approach also reduces the size of the training data set. In this study, we apply support vector machines (SVMs) after obtaining clusters with the proposed clustering algorithm. The proposed clustering algorithm is applied with different strategies for applying SVMs. The results for several real data sets show that the performance is comparable with the standard SVM while reducing the size of the training data set and also the number of support vectors.

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Section: Application To Real Data Sets and Performancementioning

confidence: 99%