An empirical study of a simple incremental classifier based on vector quantization and adaptive resonance theory - Czmil, Sylwester; Kluska, Jacek; Czmil, Anna (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: incremental learning; data classification; vector quantization; adaptive resonance theory; classification performance