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An empirical study of a simple incremental classifier based on vector quantization and adaptive resonance theory

Czmil Sylwester, Kluska Jacek, Czmil Anna

International Journal of Applied Mathematics and Computer Science

2024

Vol. 34, no. 1

149--165

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.

Sieci neuronowe a metody statystyczne

Staniec I.

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Łódzka

2000

z. 34

31-42

Istnieje wiele opracowań na temat sztucznych sieci neuronowych [SSN]. najpopularniejszą siecią neuronową jest percepton wielowarstwowy, a jest to nic innego jak nieliniowa regresja i analiza dyskryminacyjna, które można zaimplementować przy uzyciu standardowego oprogramowania statystycznego. Niniejszy artykuł ma na celu pokazanie niektórych związków pomiędzy sieciami neuronowym, a modelami statystycznymi.

The ability of neural networks to learn and generalize has gained massive publicity. In fact, the most commonly used artificial neural networks, called multilayer perceptions, are nothing more than nonlinear regression and discriminate models that can be implemented with standard statistical software.