Download PDFOpen PDF in browserA Possibilistic Fuzzy Approach for Novelty Detection with Automatically Adjusted Number of Clusters12 pages•Published: September 20, 2022AbstractThis paper introduces a new modification of the Possibilistic Fuzzy multiclass Novelty Detector for data streams (PFuzzND). Mentioned modification is based on the implementation of the automated adjustment of the number of clusters for each class (determined beforehand or during the novelty detection procedure) to improve algorithm’s ability to divide objects into small groups. As result, the proposed approach generates models with flexible class boundaries, which are capable to identify new classes or extensions of the ones that are already known as well as the outliers. Proposed possibilistic fuzzy algorithm for novelty detection was used to solve various benchmark problems with synthetically generated datasets. In order to show the workability and efficiency of the introduced modification its results were also compared with the results obtained by the original PFuzzND algorithm. Thus, it was established that the PFuzzND technique with automatically adjusted number of clusters allows achieving better results in regards to the accuracy, the Macro F-score metric and the unknown rate measure. Comparison to the original algorithm showed that the proposed modification outperforms it but is sensitive to the parameter settings, which can be also said about the PFuzzND method. Therefore, the MPFuzzND approach can be used instead of the original PFuzzND algorithm for other classification problems.Keyphrases: classification, clustering, fuzzy systems, novelty detection In: Tokuro Matsuo (editor). Proceedings of 11th International Congress on Advanced Applied Informatics, vol 81, pages 65-76.
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