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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

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2024

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Vol. 34, no. 1

149--165

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.

2

The influence of air inlet layout on the inner flow field for a vertical turbo air classifier

Yu Yuan, Li Xingshuai, Zhang Yu, Jiao Zhiwei, Liu Jiaxiang

Physicochemical Problems of Mineral Processing

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2023

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Vol. 59, iss. 6

art. no. 175859

EN

In this study, the influence of air inlet layout on the flow field distribution and particle movement trajectory for the vertical turbo air classifier are analyzed comparatively using the numerical simulation method. The air inlet layout adjustment can increase the axial velocity and turbulent dissipation rate at the feeding inlet and do not generate the axial negative velocity, which improves powder material pneumatic transportation and dispersion capacity; the air inlet layout adjustment can match the airflow rotation direction with the rotation direction of the rotor cage, which can eliminate the vortices in the rotor cage channel effectively. Moreover, the particle movement time is shortened and fast classification is completed, which can decrease the particle agglomeration probability and weaken the ‘fish-hook’ effect. The optimization scheme of the air inlet layout is Type-BC. In accordance with the numerical simulation results, the calcium carbonate classification experimental results indicate that the classification performance of the classifier is improved using Type-BC.

3

Application of Rule Induction and Rough Sets to Verification of Magnetic Resonance Diagnosis

Słowiński K., Stefanowski J., Siwiński D.

Fundamenta Informaticae

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2002

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Vol. 53, nr 3,4

345-363

EN

We discuss a process of analysing medical diagnostic data by means of the combined rule induction and rough set approach. The first step of this analysis includes the use of various techniques for discretization of numerical attributes. Rough sets theory is applied to determine attribute importance for the patients' classification. The novel contribution concerns considering two different algorithms inducing either minimum or satisfactory set of decision rules. Verification of classification abilities of these rule sets is extended by an examination of sensitivity and specificity measures. Moreover, a comparative study of these composed approaches against other learning systems is discussed. The approach is illustrated on a medical problem concerning anterior cruciate ligament (ACL) rupture in a knee. The patients are described by attributes coming from anamnesis, MR examinations and verified by arthroscopy. The clinical impact of our research is indicating two attributes (PCL index, age) and their specific values that could support a physician in resigning from performing arthroscopy for some patients.