#5796. The scope of applicability of the selected class-modelling methods

July 2026publication date
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Journal’s subject area:
Spectroscopy;
Analytical Chemistry;
Computer Science Applications;
Software;
Process Chemistry and Technology;
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Abstract:
Class-modelling methods are applied to construct a mathematical model based on the similarities among samples belonging to the same category, i.e., the target class. This model is used to study the belongingness of a new sample to the class for which the model was constructed. If the sample is recognised as not belonging to the target class, it is considered as an outlier. Therefore, the class-modelling techniques are widely used for food or drug authentication and confirmation of the product origin, in order to detect samples of poor quality or potential counterfeits. Structure of the target class data might suggest which of the available class-modelling approaches is well suited for model construction. Data structure can generally be described as normal, or heterogeneous. Normal structure exhibits Gaussian distribution, whereas heterogeneous data deviates from normal distribution, e.g., the objects can have multimodal distributions, create subgroups, and form complex shapes in the feature space. Class-modelling of the normally structured data can directly be performed on an original dataset, whereas heterogeneous datasets are usually subjected to kernel transformation prior to modelling. In this study, several datasets of various structures are analysed with different class-modelling methods to test their scope of applicability and the pros and cons from the practical point of view.
Keywords:
Kernel transformation; Multimodal data; One-class classification; Potential functions method; Support vector domain description

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