The paper "The problem of concept drift: definitions and related work" by Alexey Tsymbol, published in 2004, addresses the challenge of concept drift in machine learning. Concept drift refers to the change in underlying data distribution over time, which can render models built on old data inconsistent with new data. The paper discusses different types of concept drift, including sudden and gradual drift, and the distinction between real and virtual concept drift. It highlights the importance of distinguishing between true concept drift and noise, and the need for learners to be robust to noise while being sensitive to concept drift. The paper reviews existing approaches to handling concept drift, such as instance selection, instance weighting, and ensemble learning. Instance selection involves selecting relevant instances from a moving window, while instance weighting uses algorithms like Support Vector Machines (SVMs) to process weighted instances. Ensemble learning maintains a set of concept descriptions and combines their predictions using voting or weighted voting. The paper also discusses the use of various base learning algorithms for handling concept drift, including rule-based learning, decision trees, Naïve Bayes, SVMs, and instance-based learning. It emphasizes the advantages of lazy learning for local concept drift, as it can adapt well to changes in specific parts of the data. Benchmark datasets for testing concept drift handling systems are discussed, including the STAGGER concepts and the moving hyperplane problem. The paper notes that while these benchmarks allow control over the type and rate of concept drift, they may not fully reflect real-world scenarios due to the lack of large-scale data streams. Theoretical results in handling concept drift are also reviewed, including bounds on the rate and extent of drift that can be tolerated. The paper concludes by emphasizing the importance of incremental (online) learning for handling concept drift, as it is more suitable for real-world data processing. It also highlights the need for criteria to detect crucial changes that allow the model to adapt only when necessary, suggesting that current "triggers" are not robust enough for different types of concept drift and noise levels.The paper "The problem of concept drift: definitions and related work" by Alexey Tsymbol, published in 2004, addresses the challenge of concept drift in machine learning. Concept drift refers to the change in underlying data distribution over time, which can render models built on old data inconsistent with new data. The paper discusses different types of concept drift, including sudden and gradual drift, and the distinction between real and virtual concept drift. It highlights the importance of distinguishing between true concept drift and noise, and the need for learners to be robust to noise while being sensitive to concept drift. The paper reviews existing approaches to handling concept drift, such as instance selection, instance weighting, and ensemble learning. Instance selection involves selecting relevant instances from a moving window, while instance weighting uses algorithms like Support Vector Machines (SVMs) to process weighted instances. Ensemble learning maintains a set of concept descriptions and combines their predictions using voting or weighted voting. The paper also discusses the use of various base learning algorithms for handling concept drift, including rule-based learning, decision trees, Naïve Bayes, SVMs, and instance-based learning. It emphasizes the advantages of lazy learning for local concept drift, as it can adapt well to changes in specific parts of the data. Benchmark datasets for testing concept drift handling systems are discussed, including the STAGGER concepts and the moving hyperplane problem. The paper notes that while these benchmarks allow control over the type and rate of concept drift, they may not fully reflect real-world scenarios due to the lack of large-scale data streams. Theoretical results in handling concept drift are also reviewed, including bounds on the rate and extent of drift that can be tolerated. The paper concludes by emphasizing the importance of incremental (online) learning for handling concept drift, as it is more suitable for real-world data processing. It also highlights the need for criteria to detect crucial changes that allow the model to adapt only when necessary, suggesting that current "triggers" are not robust enough for different types of concept drift and noise levels.