<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>When constructing a new data classification algorithm, relevant quality indices such as classification accuracy (ACC) or the area under the receiver operating characteristic curve (AUC) should be investigated. End-users of these algorithms are interested in high values of the metrics as well as the proposed algorithm’s understandability and transparency. In this paper, a simple evolving vector quantization (SEVQ) algorithm is proposed, which is a novel supervised incremental learning classifier. Algorithms from the family of adaptive resonance theory and learning vector quantization inspired this method. Classifier performance was tested on 36 data sets and compared with 10 traditional and 15 incremental algorithms. SEVQ scored very well, especially among incremental algorithms, and it was found to be the best incremental classifier if the quality criterion is the AUC. The Scott–Knott analysis showed that SEVQ is comparable in performance to traditional algorithms and the leading group of incremental algorithms. The Wilcoxon rank test confirmed the reliability of the obtained results. This article shows that it is possible to obtain outstanding classification quality metrics while keeping the conceptual and computational simplicity of the classification algorithm.</p>
</abstract>

When constructing a new data classification algorithm, relevant quality indices such as classification accuracy (ACC) or the area under the receiver operating characteristic curve (AUC) should be investigated. End-users of these algorithms are interested in high values of the metrics as well as the proposed algorithm’s understandability and transparency. In this paper, a simple evolving vector quantization (SEVQ) algorithm is proposed, which is a novel supervised incremental learning classifier. Algorithms from the family of adaptive resonance theory and learning vector quantization inspired this method. Classifier performance was tested on 36 data sets and compared with 10 traditional and 15 incremental algorithms. SEVQ scored very well, especially among incremental algorithms, and it was found to be the best incremental classifier if the quality criterion is the AUC. The Scott–Knott analysis showed that SEVQ is comparable in performance to traditional algorithms and the leading group of incremental algorithms. The Wilcoxon rank test confirmed the reliability of the obtained results. This article shows that it is possible to obtain outstanding classification quality metrics while keeping the conceptual and computational simplicity of the classification algorithm.

An Empirical Study of a Simple Incremental Classifier Based on Vector Quantization and Adaptive Resonance Theory

Department of Control and Computer Engineering

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

When constructing a new data classification algorithm, relevant quality indices such as classification accuracy (ACC) or the area under the receiver operating...

An Empirical Study of a Simple Incremental Classifier Based on Vector Quantization and Adaptive Resonance Theory

Published Online: Mar 26, 2024

Page range: 149 - 165

Received: Jun 01, 2023

Accepted: Sep 24, 2023

DOI: https://doi.org/10.61822/amcs-2024-0011

Keywords
incremental learning, data classification, vector quantization, adaptive resonance theory, classification performance

© 2024 Sylwester Czmil et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An Empirical Study of a Simple Incremental Classifier Based on Vector Quantization and Adaptive Resonance Theory

Published Online: Mar 26, 2024

Page range: 149 - 165

Received: Jun 01, 2023

Accepted: Sep 24, 2023

DOI: https://doi.org/10.61822/amcs-2024-0011

Keywordsincremental learning, data classification, vector quantization, adaptive resonance theory, classification performance

© 2024 Sylwester Czmil et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
incremental learning, data classification, vector quantization, adaptive resonance theory, classification performance