Concept drift -the change of the distribution over timeposes significant challenges for learning systems and is of central interest for monitoring.Understanding drift is thus paramount, and drift localization -determining which samples are affected by the drift -is essential.While several approaches exist, most rely on local testing schemes, which tend to fail in high-dimensional, low-signal settings.In this work, we consider a fundamentally different approach based on conformal predictions.We discuss and show the shortcomings of common approaches and demonstrate the performance of our approach on state-of-the-art image datasets.

Prototype-based methods constitute a robust and transparent family of machine-learning models.To increase robustness in real-world applications, they are frequently coupled with reject options.While the state-of-the-art method, relative similarity, couples the rejection of samples with high aleatoric and epistemic uncertainty, the technique lacks transparency, i.e., an explanation of why a sample has been rejected.In this work, we analyze the relative similarity analytically and derive an explanation scheme for reject options in prototype-based classification.

Hyperspectral imaging is a suitable measurement tool across domains.However, when combined with machine learning techniques, frequently intensity and transversal shifts hinder the transfer between different sensors and settings.Established approaches focus on eliminating sensor shifts in the data or recalibrating sensors.In this contribution, we target the training procedure, propose robust training, and derive a robust feature selection strategy that can cope with multiple shift dynamics at the same time.We evaluate our approaches experimentally on artificial and real-world datasets.

Hinder F, Vaquet V, Brinkrolf J, Hammer B. On the Hardness and Necessity of Supervised Concept Drift Detection. In: De Marsico M, Sanniti di Baja G, Fred A, eds. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. Setúbal: SCITEPRESS - Science and Technology Publications; 2023: 164-175.

Concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models can become inaccurate and need adjustment. While there do exist methods to detect concept drift or to adjust models in the presence of observed drift, the question of explaining drift, i.e., describing the potentially complex and high dimensional change of distribution in a human-understandable fashion, has hardly been considered so far. This problem is of importance since it enables an inspection of the most prominent characteristics of how and where drift manifests itself. Hence, it enables human understanding of the change and it increases acceptance of life-long learning models. In this paper, we present a novel technology characterizing concept drift in terms of the characteristic change of spatial features based on various explanation techniques. To do so, we propose a methodology to reduce the explanation of concept drift to an explanation of models that are trained in a suitable way to extract relevant information regarding the drift. This way, a large variety of explanation schemes is available. Thus, a suitable method can be selected for the problem of drift explanation at hand. We outline the potential of this approach and demonstrate its usefulness in several examples.

Federated learning is an efficient methodology to reduce the data transmissions to the server when working with large amounts of (sen- sor) data from diverse physical locations. When using data from different sensor devices concept drift between the single sensors poses an additional challenge. In this contribution we define a formal framework for federated learning with concept drift and propose a version of federated LVQ dealing with concept drift induced by different hyperspectral cameras. We evalu- ate this approach experimentally and demonstrate its robustness to class imbalance and missing classes.

The notion of concept drift refers to the phenomenon that the distribution which is underlying the observed data changes over time. As a consequence machine learning models may become inaccurate and need adjustment. While there do exist methods to detect concept drift, to find change points in data streams, or to adjust models in the presence of observed drift, the problem of localizing drift, i.e. identifying it in data space, is yet widely unsolved - in particular from a formal perspective. This problem however is of importance, since it enables an inspection of the most prominent characteristics, e.g. features, where drift manifests itself and can therefore be used to make informed decisions, e.g. efficient updates of the training set of online learning algorithms, and perform precise adjustments of the learning model. In this paper we present a general theoretical framework that reduces drift localization to a supervised machine learning problem. We construct a new method for drift localization thereon and demonstrate the usefulness of our theory and the performance of our algorithm by comparing it to other methods from the literature.

Brinkrolf J, Hammer B. Federated Learning Vector Quantization. In: Verleysen M, ed. Proceedings of the ESANN, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Accepted.