The Internet of Things generates massive data streams, with edge computing emerging as a key enabler for online IoT applications and 5G networks. Edge solutions facilitate real-time machine learning inference, but also require continuous adaptation to concept drifts. While extensions of the Very Fast Decision Tree (VFDT) remain state-of-the-art for tabular stream mining, their unregulated growth limit efficiency, particularly in ensemble settings where post-pruning at the individual tree level is seldom applied. This paper presents DFDT, a novel memory-constrained algorithm for online learning. DFDT employs activity-aware pre-pruning, dynamically adjusting splitting criteria based on leaf node activity: low-activity nodes are deactivated to conserve resources, moderately active nodes split under stricter conditions, and highly active nodes leverage a skipping mechanism for accelerated growth. Additionally, adaptive grace periods and tie thresholds allow DFDT to modulate splitting decisions based on observed data variability, enhancing the accuracy–memory–runtime trade-off while minimizing the need for hyperparameter tuning. An ablation study reveals three DFDT variants suited to different resource profiles. Fully compatible with existing ensemble frameworks, DFDT provides a drop-in alternative to standard VFDT-based learners.

State-of-the-art data stream mining has long drawn from ensembles of the Very Fast Decision Tree, a seminal algorithm honored with the 2015 KDD Test-of-Time Award. However, the emergence of large tabular models, i.e., transformers designed for structured numerical data, marks a significant paradigm shift. These models move beyond traditional weight updates, instead employing in-context learning through prompt tuning. By using on-the-fly sketches to summarize unbounded streaming data, one can feed this information into a pre-trained model for efficient processing. This work bridges advancements from both areas, highlighting how transformers' implicit meta-learning abilities, pre-training on drifting natural data, and reliance on context optimization directly address the core challenges of adaptive learning in dynamic environments. Exploring real-time model adaptation, this research demonstrates that TabPFN, coupled with a simple sliding memory strategy, consistently outperforms ensembles of Hoeffding trees, such as Adaptive Random Forest, and Streaming Random Patches, across all non-stationary benchmarks.

Mining data streams with multi-label outputs poses significant challenges due to evolving distributions, high-dimensional label spaces, sparse label occurrences, and complex label dependencies. Moreover, concept drift affects not only input distributions but also label correlations and imbalance ratios over time, complicating model adaptation. To address these challenges, structured learners are categorized into local and global methods. Local methods break down the task into simpler components, while global methods adapt the algorithm to the full output space, potentially yielding better predictions by exploiting label correlations. This work introduces iHOMER (Incremental Hierarchy Of Multi-label Classifiers), an online multi-label learning framework that incrementally partitions the label space into disjoint, correlated clusters without relying on predefined hierarchies. iHOMER leverages online divisive-agglomerative clustering based on Jaccard similarity and a global tree-based learner driven by a multivariate Bernoulli process to guide instance partitioning. To address non-stationarity, it integrates drift detection mechanisms at both global and local levels, enabling dynamic restructuring of label partitions and subtrees. Experiments across 23 real-world datasets show iHOMER outperforms 5 state-of-the-art global baselines, such as MLHAT, MLHT of Pruned Sets and iSOUPT, by 23%, and 12 local baselines, such as binary relevance transformations of kNN, EFDT, ARF, and ADWIN bagging/boosting ensembles, by 32%, establishing its robustness for online multi-label classification.

Abstract The railway sector has witnessed a significant surge in condition-based maintenance, thanks to the proliferation of sensing technologies and data-driven methodologies, such as machine learning. However, despite the plethora of algorithms designed to detect and classify track irregularities and wheel out-of-roundness, they often fall short when put to the test in real-world scenarios. These shortcomings typically stem from their inability to meet all four critical requirements for constructing an effective maintenance plan: (R1) suitability of the condition-based maintenance strategy, (R2) availability of relevant data, (R3) proper problem formulation, and (R4) accurate evaluation of data mining methods. In response to the absence of a unified framework and standardized guidelines, this survey delves into the realm of time series sensor data and wheel-track interface components for railway structural health monitoring. This survey aims to bridge this gap by offering an extensive categorization, pinpointing existing challenges, and outlining potential directions for future research. Through these efforts, this survey provides a more thorough and targeted exploration of the subject matter, contributing to the advancement of this field.

Within the big data paradigm, there is an increasing demand for machine learning with automatic configuration of hyperparameters.Although several algorithms have been proposed for automatically learning time-changing concepts, they generally do not scale well to very large databases.In this context, this paper presents an automated green machine learning approach applied to condition-based maintenance with automatic data fusion and density-based anomaly detection based on locality sensitivity hashing.Experiments on numerical simulations of train-track dynamic interactions demonstrate the utility of the approach to detect railway wheel out-of-roundness.This unlocks the full potential of scalable machine learning, paving the way for environment-friendly systems and automated decision-making.

Industry 4.0. has empowered the integration of real-time sensor readings and process knowledge for early identification of faults. In this paper, simulation was used to evaluate the process of classifying data streams as drifting towards defective behavior. Focusing on the challenges imposed by real-word, unlabeled and non-stationary data, the proposed method was assessed by conducting a case-study in the plastic extrusion domain. The knowledge gained from initial tests on generated synthetic data sets was used to isolate symptoms of a clogged screen pack from the concept drifts detected by the Adaptive Windowing algorithm. Experimental results for 40 days of running time of an extruder show that the proposed method helped in identifying a defective screen pack state upon the appearance of defects in the extrudate.