In this paper, we propose practical Python programs together with appropriate environments for the analysis of data stream drift, including the analysis of feature drift. The proposed approach contains a description of both, synthetic and real datasets. These datasets include different types of drifts like sudden, incremental, or gradual. Also, the location of the drift can be programmed. Additionally, we propose Java scripts that allow specifying the number and place of drift locations. The software is focused on prequential error methodology. Our proposition can be used by scientists interested in machine learning and concept drift detection because the proposed solution makes it easier to conduct practical experiments on that matter. The proposed solution allows for conducting experiments in a homogeneous programming environment. Versions of Python programs, stored in the GitHub repository, contain implementations of popular classifiers and drift detectors. The GitHub repository is located in the Public Domain.

The article presents a new method of multibiometric Verification that has been enhanced with dynamic classifier selection based on determining their competence. The competence of a classifier is defined, taking into account the type of biometric trait and the samples analyzed. The proposed approach not only allows for adaptive selection of classifiers to specific features and samples but also increases system efficiency by more effectively utilizing models that best match the given conditions. Classifier selection occurs dynamically, which enables the system to adjust flexibly to changing conditions and leverage the strengths of individual models while mitigating their weaknesses through compensation within the committee. The effectiveness of this method has been verified through experiments, which confirmed a significant improvement in person Verification effectiveness compared to methods without dynamic classifier selection.

The article presents a new concept drift detection method based on analyzing the importance of features of instances in the data stream. The data stream contains information about distribution patterns that reflect different concepts that may be hidden in the data stream. The presented drift detector concept uses information about the fluctuation of the most informative feature inside chunks of the data stream and compares it with the change of the same feature in neighbor chunks. In the case of data streams, the meaning of features can change over time. These changes affect the quality of the classification but can also be a significant indicator of ongoing concept drift. After detecting the drift, the classifier should be trained with the new dataset. But this issue is not addressed in this article. In this work, we propose a new concept drift detector in the data stream for the first time. This goal is achieved by observing the changing importance of features in different parts of the data stream. The proposed approach uses the feature significance measure as a drift detector. The obtained results indicate that the method can be introduced in practice. Because these are only preliminary results, in this paper, we focused on presenting the advantages of our strategy without comparison with other methods.

Riskful and aggressive driving are significant problems in road transportation. Numerous studies prove drivers’ behavior is critical in most road accidents and contributes significantly to fuel consumption and emissions. In an attempt to improve road safety, it is important to be able to detect such unskilful and irresponsible drivers on a mass scale. Thus an automated onboard driving safety assessment system is required. Transmitting amounts of telemetric data required to assess driving style is unpractical and economically unjustified, thus some data processing must take place in an Onboard Unit (OBU). As a first step in driving safety assessment is detecting maneuvers, this is the task, that can be done in the OBU. In this paper, we propose a Finite State Machine based algorithm, which could run on OBU and allows marking of basic maneuvers in the telemetry datastream. This allows the calculation of additional features, aggregation, and transmission of data useful for classification the of driving safety, and thus a vast reduction in data volume sent over the air. Also, an example of detecting unsafe maneuver using such aggregated data is given.

In recent years, there has been an increasing amount of streaming information coming from time series. Learning from data appearing in real time is quite a call, due in part to the speed at which new data appears. Hidden data changes that are not previously known to learning algorithms are referred to in the literature as data or concept drift. In classical machine learning, a classifier analyzes new data using past training instances of the data stream. However, the accuracy of the classifier deteriorates due to data drift, which occurs in non-stationary data. In such situations, the classifier must detect a significant change in the data adapt its prediction over time. The motivation of this paper is to show a method for drift detection without knowledge of instance labels. Labels are sometimes not available or periodically missing, making it difficult to apply methods where knowledge of them is required.

This paper describes a novel method for classification based on a partly incomplete and low quality image data. The method has been tested on images with low quality areas such as partially corrupted, dark, or blurry images. Image data are converted to classifier input. As a result, we are dealing with missing data describing these areas, but the classifier was designed to work in the presence of missing data. The analysis of the literature shows that missing data have a negative impact on the operation of the learning algorithms. This field is still insufficient explored. In our method, we integrate complex image processing techniques, machine learning, and statistical methods under various contexts. The novelty of the presented approach is the structure of the classifier, which works in the presence of missing data on different principles than those used so far. Evaluation was performed on the basis of realistic images, collected before experiments. Multi-variant experimental protocols, specially designed in this work, confirmed the accuracy of the method. The obtained results help to better understand the methods of recognizing objects with incomplete data structures. Analysis techniques proposed in this paper are rapidly gaining in importance, so the proposed approach can be useful in many areas where the machine learning is employed.

The paper proposes a novel signature verification concept. This new approach uses appropriate similarity coefficients to evaluate the associations between the signature features. This association, called the new composed feature, enables the calculation of a new form of similarity between objects. The most important advantage of the proposed solution is case-by-case matching of similarity coefficients to a signature features, which can be utilized to assess whether a given signature is genuine or forged. The procedure, as described, has been repeated for each person presented in a signatures database. In the verification stage, a two-class classifier recognizes genuine and forged signatures. In this paper, a broad range of classifiers are evaluated. These classifiers all operate on features observed and computed during the data preparation stage. The set of signature composed features of a given person can be reduced what decrease verification error. Such a phenomenon does not occur for the raw features. The approach proposed was tested in a practical environment, with handwritten signatures used as the objects to be compared. The high level of signature recognition obtained confirms that the proposed methodology is efficient and that it can be adapted to accommodate as yet unknown features. The approach proposed can be incorporated into biometric systems.

The paper presents a new fuzzy approach to off-line handwritten signature recognition. The solution is based on characteristic feature extraction. After finding signature’s center of gravity a number of lines are drawn through it at different angles. Cross points of generated lines and signature sample, which are further grouped and sorted, are treated as the set of features. On the basis of such structures, obtained from a chosen number of learning samples, a fuzzy model is created, called the fuzzy signature. During a verification phase the level of conformity of an input sample and the fuzzy signature is calculated. The extension in feature extraction as well as proposed fuzzy model has never been employed before. It needs to be emphasized that information stored within the verification system cannot be used to recreate the original signatures collected at the enrolment phase. The fact is particularly valuable for large databases and systems where storage safety is crucial. The solution is very flexible and allows the user to extend an intuitive structure of fuzzy sets by employing dynamic features, making the approach an on-line method. The results obtained should be still improved, similarly to the case of other known biometric systems related to signature recognition. However, the presented technique can be easily utilized in applications where FAR coefficient should be very low and is more important than FRR ratio.