A New Interpretability Criteria for Neuro-Fuzzy Systems for Nonlinear Classification

Łapa, Krystian; Cpałka, Krzysztof; Galushkin, A.I.

doi:10.1007/978-3-319-19324-3_41

Cited by 12 publications

(1 citation statement)

References 94 publications

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“…The evolutionary algorithms belong to the group of population-based algorithms (Population-Based Algorithms, PBAs [10,11,16,19,36,38]). They are widely used to solve problems in which the evaluation function may be non-differentiable [18].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach

Zalasiński

Laskowski

Niksa-Rynkiewicz

et al. 2022

Journal of Artificial Intelligence and Soft Computing Research

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In the verification of identity, the aim is to increase effectiveness and reduce involvement of verified users. A good compromise between these issues is ensured by dynamic signature verification. The dynamic signature is represented by signals describing the position of the stylus in time. They can be used to determine the velocity or acceleration signal. Values of these signals can be analyzed, interpreted, selected, and compared. In this paper, we propose an approach that: (a) uses an evolutionary algorithm to create signature partitions in the time and velocity domains; (b) selects the most characteristic partitions in terms of matching with reference signatures; and (c) works individually for each user, eliminating the need of using skilled forgeries. The proposed approach was tested using Biosecure DS2 database which is a part of the DeepSignDB, a database with genuine dynamic signatures. Our simulations confirmed the correctness of the adopted assumptions.

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