This paper provides a comprehensive survey of ensemble learning methods for data stream analysis, focusing on classification and regression tasks. It highlights the challenges posed by dynamic environments where data streams are continuously updated, requiring algorithms to process new examples incrementally while using limited memory and time. The paper discusses the importance of adapting to concept drifts, which are changes in the distribution of data over time, and reviews various ensemble techniques designed to address these challenges. Key topics include: 1. **Data Stream Characteristics**: The paper covers the nature of data streams, including their sequential and potentially unbounded nature, and the need for algorithms that can handle evolving data distributions. 2. **Drift Detection Methods**: Various methods for detecting concept drifts, such as statistical process control, sequential analysis, and contextual approaches, are discussed. The goal is to reduce performance deterioration and minimize restoration time. 3. **Evaluation in Data Stream Analysis**: Techniques for evaluating classifiers in data streams, including holdout evaluation and prequential evaluation, are explored. The paper emphasizes the importance of considering both incremental processing and evolving data characteristics. 4. **Ensemble Learning from Data Streams**: The paper presents a taxonomy of ensemble learning approaches for data streams, categorizing them based on stationary vs. non-stationary environments, active vs. passive approaches, chunk-based vs. online learning modes, and strategies for updating component classifiers and aggregating predictions. The paper concludes with a discussion of open research problems and future directions, emphasizing the need for more advanced methods to handle complex data representations and structured outputs in data streams.This paper provides a comprehensive survey of ensemble learning methods for data stream analysis, focusing on classification and regression tasks. It highlights the challenges posed by dynamic environments where data streams are continuously updated, requiring algorithms to process new examples incrementally while using limited memory and time. The paper discusses the importance of adapting to concept drifts, which are changes in the distribution of data over time, and reviews various ensemble techniques designed to address these challenges. Key topics include: 1. **Data Stream Characteristics**: The paper covers the nature of data streams, including their sequential and potentially unbounded nature, and the need for algorithms that can handle evolving data distributions. 2. **Drift Detection Methods**: Various methods for detecting concept drifts, such as statistical process control, sequential analysis, and contextual approaches, are discussed. The goal is to reduce performance deterioration and minimize restoration time. 3. **Evaluation in Data Stream Analysis**: Techniques for evaluating classifiers in data streams, including holdout evaluation and prequential evaluation, are explored. The paper emphasizes the importance of considering both incremental processing and evolving data characteristics. 4. **Ensemble Learning from Data Streams**: The paper presents a taxonomy of ensemble learning approaches for data streams, categorizing them based on stationary vs. non-stationary environments, active vs. passive approaches, chunk-based vs. online learning modes, and strategies for updating component classifiers and aggregating predictions. The paper concludes with a discussion of open research problems and future directions, emphasizing the need for more advanced methods to handle complex data representations and structured outputs in data streams.