Continuous data generation over time presents new challenges for Machine Learning systems, which must develop real-time models due to memory and latency limitations. Streaming Machine Learning algorithms analyze data streams one sample at a time, progressively updating their models. However, is it necessary to utilize all the data for model updates? This paper introduces the Online Ensemble SPaced Learning (OE-SPL) strategy, an ensemble meta-strategy that combines online ensemble learning and the Spaced Learning heuristic to rapidly learn underlying concepts without using all samples. We evaluated OE-SPL on synthetic and real data streams containing various concept drifts, providing statistical evidence that OE-SPL achieves comparable performance to state-of-the-art ensemble models while recovering from multiple concept drift occurrences more efficiently, using less time and RAM-Hours.

Sports analytics has grown significantly through continuous data streams from wearable tracking devices. This article explores the performance of traditional Machine Learning methods (ML) compared to Streaming Machine Learning approaches (SML) in the context of sports analytics, specifically in identifying passes during a soccer match. The study utilizes leg movement data from wearable sensors on players’ shoes to differentiate between pass and nonpass actions. Balanced, imbalanced and rebalanced datasets are created and analyzed. Several traditional and streaming algorithms are tested, and statistical analyses are performed to assess their performance. The findings indicate that Streaming Machine Learning can achieve comparable or better performance than traditional methods, especially on larger rebalanced datasets. This research highlights the potential of Streaming Machine Learning for online sports analytics and suggests future directions for exploring player tendencies and edge device adaptability.

The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift. In this paper, we propose the Synthetic Minority Oversampling TEchnique with Online Bagging (SMOTE-OB). It is a novel cost-sensitive ensemble strategy that uses Online Bagging and a new sketched version of SMOTE to over/undersample the minority and majority classes. We benchmarked SMOTE-OB on synthetic and real data streams containing different concept drifts, imbalance levels, and class distributions. We bring statistical evidence that the SMOTE-OB ensemble achieves minority class performance that are better than the state-of-the-art ones. Moreover, we perform a time/memory consumption analysis.

Processing data streams gained much importance in recent years. Standard machine learning algorithms do not cope well with non-stationary streaming data, where decision models evolve and generate so-called concept drift. Online adaptive algorithms emerged to solve these issues. They learn incrementally and generally require explicit forgetting mechanisms to adapt to concept drift. In this paper, we propose the application of Kalman filtering to handle evolving data streams. This novel approach addresses data stream mining and concept drift management challenges from a new perspective, directly modelling a representation suitable for the data streams. First, we study a Kalman filter based learning a pproach and investigate its integration into the Naïve Bayes algorithm, namely KalmanNB. Additionally, we propose the Hoeffding Kalman Tree, a combination of the Hoeffding Tree with KalmanNB. Empirical results demonstrate that the Kalman filter based approach inherently manages concept drifts, and it adapts to the emerging concept more rapidly than the state-of-the-art algorithms. Moreover, it is an accurate and robust approach and requires less storage while still being faster.

Abstract The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift. In this paper, we propose the very fast continuous synthetic minority oversampling technique ( VFC - SMOTE ). It is a novel meta-strategy to be prepended to any streaming machine learning classification algorithm aiming at oversampling the minority class using a new version of Smote and Borderline - Smote inspired by Data Sketching. We benchmarked VFC - SMOTE pipelines on synthetic and real data streams containing different concept drifts, imbalance levels, and class distributions. We bring statistical evidence that VFC - SMOTE pipelines learn models whose minority class performances are better than state-of-the-art. Moreover, we analyze the time/memory consumption and the concept drift recovery speed.

Streaming Machine Learning (SML) studies single-pass learning algorithms that update their models one data item at a time given an unbounded and often non-stationary flow of data (a.k.a., in presence of concept drift). Online class imbalance learning is a branch of SML that combines the challenges of both class imbalance and concept drift. In this paper, we investigate the binary classification problem of rebalancing an imbalanced stream of data in the presence of concept drift, accessing one sample at a time. We propose Continuous Synthetic Minority Oversampling Technique (C-SMOTE), a novel rebalancing meta-strategy to pipeline with SML classification algorithms. C-SMOTE is inspired by the popular SMOTE algorithm but operates continuously. We benchmark C-SMOTE pipelines on ten different groups of data streams. We bring empirical evidence that models learnt with C-SMOTE pipelines outperform models trained on imbalanced data stream without losing the ability to deal with concept drifts. Moreover, we show that they outperform other stream balancing techniques from the literature.