<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In this article there is proposed a new two-parametrical variant of the gravitational classification method. We use the general idea of objects' behavior in a gravity field. Classification depends on a test object's motion in a gravity field of training points. To solve this motion problem, we use a simulation method. This classifier is compared to the 1NN method, because our method tends towards it for some parameter values. Experimental results on different data sets demonstrate an improvement in efficiency and that this approach outperforms the 1NN method by providing a significant reduction in the mean classification error rate. </p></abstract>

In this article there is proposed a new two-parametrical variant of the gravitational classification method. We use the general idea of objects' behavior in a gravity field. Classification depends on a test object's motion in a gravity field of training points. To solve this motion problem, we use a simulation method. This classifier is compared to the 1NN method, because our method tends towards it for some parameter values. Experimental results on different data sets demonstrate an improvement in efficiency and that this approach outperforms the 1NN method by providing a significant reduction in the mean classification error rate.

A variant of gravitational classification

Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznań, Poland

Faculty of Civil Engineering, Environmental and Geodetic Sciences, Koszalin University of Technology, Śniadeckich 2, 75-453 Koszalin, Poland

Biometrical Letters

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

{"article-title":"A variant of gravitational classification"}

In this article there is proposed a new two-parametrical variant of the gravitational classification method. We use the general idea of objects' behavior in a...

A variant of gravitational classification

Online veröffentlicht: 06. Juni 2014

Seitenbereich: 1 - 12

DOI: https://doi.org/10.2478/bile-2014-0001

Schlüsselwörter
machine learning, nearest neighbor method, dynamic classifier, gravitational classification

© by Tomasz Górecki

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A variant of gravitational classification

Online veröffentlicht: 06. Juni 2014

Seitenbereich: 1 - 12

DOI: https://doi.org/10.2478/bile-2014-0001

Schlüsselwörtermachine learning, nearest neighbor method, dynamic classifier, gravitational classification

© by Tomasz Górecki

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Schlüsselwörter
machine learning, nearest neighbor method, dynamic classifier, gravitational classification