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Entropic One-Class Classifiers.

Lorenzo Livi, Alireza Sadeghian, Witold Pedrycz

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    This study introduces a novel one-class classification system for outlier and anomaly detection. The method effectively models target class patterns using dissimilarity spaces and graph representations for accurate recognition.

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    Area of Science:

    • Pattern Recognition
    • Machine Learning
    • Data Mining

    Background:

    • One-class classification, also known as outlier or anomaly detection, focuses on modeling a single target class.
    • Distinguishing target patterns from non-target patterns is crucial in various applications.

    Purpose of the Study:

    • To propose a novel one-class classification system.
    • To develop a method capable of processing diverse data types through dissimilarity representation.

    Main Methods:

    • Embedding data into a dissimilarity space (DS) using a parametric dissimilarity measure.
    • Representing dissimilarity vectors with weighted Euclidean graphs to analyze data distribution entropy.
    • Employing a global optimization scheme to tune parametric dissimilarity measure based on data characteristics.

    Main Results:

    • The system derives decision regions modeled as clusters of vertices within the dissimilarity space.
    • The classifier provides both hard (Boolean) and soft decisions for pattern recognition.
    • Experimental validation on diverse datasets demonstrates the technique's effectiveness.

    Conclusions:

    • The proposed dissimilarity-based approach offers a robust solution for one-class classification.
    • The system's ability to handle various data types and provide nuanced decisions highlights its versatility.
    • The method shows significant promise for real-world anomaly detection tasks.