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Evolutionary algorithm for selecting dynamic signatures partitioning approach

Zalasiński Marcin, Laskowski Łukasz, Niksa-Rynkiewicz Tacjana, Cpałka Krzysztof, Byrski Aleksander, Przybyszewski Krzysztof, Trippner Paweł, Dong Shi

Journal of Artificial Intelligence and Soft Computing Research

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2022

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Vol. 12, No. 4

267--279

EN

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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A novel approach to type-reduction and design of interval type-2 fuzzy logic systems

Starczewski Janusz T., Przybyszewski Krzysztof, Byrski Aleksander, Szmidt Eulalia, Napoli Christian

Journal of Artificial Intelligence and Soft Computing Research

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2022

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Vol. 12, No. 3

197--206

EN

Fuzzy logic systems, unlike black-box models, are known as transparent artificial intelligence systems that have explainable rules of reasoning. Type 2 fuzzy systems extend the field of application to tasks that require the introduction of uncertainty in the rules, e.g. for handling corrupted data. Most practical implementations use interval type-2 sets and process interval membership grades. The key role in the design of type-2 interval fuzzy logic systems is played by the type-2 inference defuzzification method. In type-2 systems this generally takes place in two steps: type-reduction first, then standard defuzzification. The only precise type-reduction method is the iterative method known as Karnik-Mendel (KM) algorithm with its enhancement modifications. The known non-iterative methods deliver only an approximation of the boundaries of a type-reduced set and, in special cases, they diminish the profits that result from the use of type-2 fuzzy logic systems. In this paper, we propose a novel type-reduction method based on a smooth approximation of maximum/minimum, and we call this method a smooth type-reduction. Replacing the iterative KM algorithm by the smooth type-reduction, we obtain a structure of an adaptive interval type-2 fuzzy logic which is non-iterative and as close to an approximation of the KM algorithm as we like.

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An approach to generalization of the intuitionistic fuzzy topsis method in the framework of evidence theory

Dymova Ludmila, Kaczmarek Krzysztof, Sevastjanov Pavel, Sułkowski Łukasz, Przybyszewski Krzysztof

Journal of Artificial Intelligence and Soft Computing Research

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2021

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Vol. 11, No. 2

157--175

EN

A generalization of technique for establishing order preference by similarity to the ideal solution (TOPSIS) in the intuitionistic fuzzy setting based on the redefinition of intuitionistic fuzzy sets theory (A−IFS) in the framework of Dempster-Shafer theory (DST) of evidence is proposed. The use of DST mathematical tools makes it possible to avoid a set of limitations and drawbacks revealed recently in the conventional Atanassov’s operational laws defined on intuitionistic fuzzy values, which may produce unacceptable results in the solution of multiple criteria decision-making problems. This boosts considerably the quality of aggregating operators used in the intuitionistic fuzzy TOPSIS method. It is pointed out that the conventional TOPSIS method may be naturally treated as a weighted sum of some modified local criteria. Because this aggregating approach does not always reflects well intentions of decision makers, two additional aggregating methods that cannot be defined in the framework of conventional A−IFS based on local criteria weights being intuitionistic fuzzy values, are introduced. Having in mind that different aggregating methods generally produce different alternative rankings to obtain the compromise ranking, the method for aggregating of aggregation modes has been applied. Some examples are used to illustrate the validity and features of the proposed approach.

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On-line signature partitioning using a population based algorithm

Zalasiński Marcin, Łapa Krystian, Cpałka Krzysztof, Przybyszewski Krzysztof, Yen Gary G.

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 1

5--13

EN

The on-line signature is a biometric attribute which can be used for identity verification. It is a very useful characteristic because it is commonly accepted in societies across the world. However, the verification process using this particular biometric feature is a rather difficult one. Researchers working on identity verification involving the on-line signature might face various problems, including the different discriminative power of signature descriptors, the problem of a large number of descriptors, the problem of descriptor generation, etc. However, population-based algorithms (PBAs) can prove very useful when resolving these problems. Hence, we propose a new method for on-line signature partitioning using a PBA in order to improve the verification process effectiveness. Our method uses the Differential Evolution algorithm with a properly defined evaluation function for creating the most characteristic partitions of the dynamic signature. We present simulation results of the proposed method for the BioSecure DS2 database distributed by the BioSecure Association.

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An algorithm for the evolutionary-fuzzy generation of on-line signature hybrid descriptors

Zalasiński Marcin, Cpałka Krzysztof, Laskowski Łukasz, Wunsch II Donald C., Przybyszewski Krzysztof

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 3

173--187

EN

In biometrics, methods which are able to precisely adapt to the biometric features of users are much sought after. They use various methods of artificial intelligence, in particular methods from the group of soft computing. In this paper, we focus on on-line signature verification. Such signatures are complex objects described not only by the shape but also by the dynamics of the signing process. In standard devices used for signature acquisition (with an LCD touch screen) this dynamics may include pen velocity, but sometimes other types of signals are also available, e.g. pen pressure on the screen surface (e.g. in graphic tablets), the angle between the pen and the screen surface, etc. The precision of the on-line signature dynamics processing has been a motivational springboard for developing methods that use signature partitioning. Partitioning uses a well-known principle of decomposing the problem into smaller ones. In this paper, we propose a new partitioning algorithm that uses capabilities of the algorithms based on populations and fuzzy systems. Evolutionary-fuzzy partitioning eliminates the need to average dynamic waveforms in created partitions because it replaces them. Evolutionary separation of partitions results in a better matching of partitions with reference signatures, eliminates disproportions between the number of points describing dynamics in partitions, eliminates the impact of random values, separates partitions related to the signing stage and its dynamics (e.g. high and low velocity of signing, where high and low are imprecise-fuzzy concepts). The operation of the presented algorithm has been tested using the well-known BioSecure DS2 database of real dynamic signatures.

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A novel drift detection algorithm based on features’ importance analysis in a data streams environment

Duda Piotr, Przybyszewski Krzysztof, Wang Lipo

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 4

287--298

EN

The training set consists of many features that influence the classifier in different degrees. Choosing the most important features and rejecting those that do not carry relevant information is of great importance to the operating of the learned model. In the case of data streams, the importance of the features may additionally change over time. Such changes affect the performance of the classifier but can also be an important indicator of occurring concept-drift. In this work, we propose a new algorithm for data streams classification, called Random Forest with Features Importance (RFFI), which uses the measure of features importance as a drift detector. The RFFT algorithm implements solutions inspired by the Random Forest algorithm to the data stream scenarios. The proposed algorithm combines the ability of ensemble methods for handling slow changes in a data stream with a new method for detecting concept drift occurrence. The work contains an experimental analysis of the proposed algorithm, carried out on synthetic and real data.