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

• Autorzy: Czmil Sylwester , Kluska Jacek , Czmil Anna

Identyfikatory

DOI

10.61822/amcs-2024-0011

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

incremental learning; data classification; vector quantization; adaptive resonance theory; classification performance

PL

uczenie przyrostowe; klasyfikacja danych; kwantyzacja wektorowa; teoria rezonansu adaptacyjnego

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2024
• Tom: Vol. 34, no. 1
• Strony: 149--165
• Opis fizyczny: Bibliogr. 62 poz., rys., tab., wykr.

Twórcy

autor

Czmil Sylwester

• Department of Control and Computer Engineering, Rzeszow University of Technology, Powstańców Warszawy 12, 35-959 Rzeszów, Poland

autor

Kluska Jacek

• Department of Control and Computer Engineering, Rzeszow University of Technology, Powstańców Warszawy 12, 35-959 Rzeszów, Poland

autor

Czmil Anna

• Department of Control and Computer Engineering, Rzeszow University of Technology, Powstańców Warszawy 12, 35-959 Rzeszów, Poland

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).