Learning in the presence of concept drift and hidden contexts
Gerhard Widmer,Miroslav Kubat +1 more
TLDR
A family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear are described, including a heuristic that constantly monitors the system's behavior.Citations
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Journal ArticleDOI
An iterative boosting-based ensemble for streaming data classification
TL;DR: Results comparing the proposed ensemble-based algorithm against eight other ensembles found in the literature show that the proposed algorithm is very competitive when dealing with data stream classification.
Proceedings ArticleDOI
An ensemble-based approach to fast classification of multi-label data streams
Xiangnan Kong,Philip S. Yu +1 more
TL;DR: Empirical studies on real-world tasks demonstrate that the proposed method can maintain a high accuracy in multi-label stream classification, while providing a very efficient solution to the task.
Journal ArticleDOI
Recurring and Novel Class Detection Using Class-Based Ensemble for Evolving Data Stream
Tahseen M. Al-Khateeb,Mohammad M. Masud,Khaled Al-Naami,Sadi Evren Seker,Ahmad Mustafa,Latifur Khan,Zouheir Trabelsi,Charu C. Aggarwal,Jiawei Han +8 more
TL;DR: This paper defines a novel ensemble technique “class-based” ensemble which replaces the traditional “chunk- based” approach in order to detect the recurring classes in data streams and proves the superiority of both “ class-based" ensemble method over state-of-art techniques via empirical approach on a number of benchmark data sets.
Journal Article
Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners
TL;DR: An incremental decision tree that is updated with incoming examples and is better than evaluated methods in its ability to deal with concept drift when dealing with problems in which: concept change occurs at different speeds, noise may be present and, examples may arrive from different areas of the problem domain.
Journal ArticleDOI
Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection
TL;DR: The EXPected Similarity Estimation (EXPoSE) as discussed by the authors is a kernel-based method for anomaly detection on very large datasets and data streams, which is able to efficiently compute the similarity between new data points and the distribution of regular data.
References
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Proceedings ArticleDOI
A theory of the learnable
TL;DR: This paper regards learning as the phenomenon of knowledge acquisition in the absence of explicit programming, and gives a precise methodology for studying this phenomenon from a computational viewpoint.
Journal ArticleDOI
Instance-Based Learning Algorithms
TL;DR: This paper describes how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy and extends the nearest neighbor algorithm, which has large storage requirements.
Book
Machine Learning: An Artificial Intelligence Approach
TL;DR: This book contains tutorial overviews and research papers on contemporary trends in the area of machine learning viewed from an AI perspective, including learning from examples, modeling human learning strategies, knowledge acquisition for expert systems, learning heuristics, discovery systems, and conceptual data analysis.
Journal ArticleDOI
Learnability and the Vapnik-Chervonenkis dimension
TL;DR: This paper shows that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned.
Journal ArticleDOI
Queries and Concept Learning
TL;DR: This work considers the problem of using queries to learn an unknown concept, and several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries.