Geometrical Modification of Learning Vector Quantization Method for Solving Classification Problems

Year 2016, Volume: 20 Issue: 3, 414 - 420, 08.09.2016

Korhan Günel , Rıfat Aşlıyan , İclal Gör

https://doi.org/10.19113/sdufbed.22419

Cited By: 2

https://izlik.org/JA35BH35FZ

Abstract

In this paper, a geometrical scheme is presented to show how to overcome an encountered problem arising from the use of generalized delta learning rule within competitive learning model. It is introduced a theoretical methodology for describing the quantization of data via rotating prototype vectors on hyper-spheres.
The proposed learning algorithm is tested and verified on different multidimensional datasets including a binary class dataset and two multiclass datasets from the UCI repository, and a multiclass dataset constructed by us. The proposed method is compared with some baseline learning vector quantization variants in literature for all domains. Large number of experiments verify the performance of our proposed algorithm with acceptable accuracy and macro f1 scores.

Keywords

Machine learning , Learning vector quantization; Geometrical learning approach

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