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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Adaptation to Drifting User's Interests
Ingo Schwab,Ivan Koychev +1 more
TL;DR: Experiments with a recommender system show that the gradual forgetting improves the ability to adapt to drifting user's interests and experiments with the STAGGER problem provide additional evidences that gradual forgetting is able to improve the prediction accuracy on drifting concepts.
Journal ArticleDOI
A Classifier Graph Based Recurring Concept Detection and Prediction Approach.
TL;DR: A novel paradigm was proposed for capturing and exploiting recurring concepts in data streams that not only incorporates a distribution-based change detector for handling concept drift but also captures recurring concept by storing recurring concept in a classifier graph.
Proceedings ArticleDOI
Credit card fraud detection and concept-drift adaptation with delayed supervised information
TL;DR: This paper designs two FDSs on the basis of an ensemble and a sliding-window approach and shows that the winning strategy consists in training two separate classifiers (on feedbacks and delayed labels, respectively), and then aggregating the outcomes.
Journal ArticleDOI
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
TL;DR: An efficient and effective system RePro is proposed to implement new ideas in prediction in data streams, which uses a measure of conceptual equivalence to organize the data history into a history of concepts and incorporates proactive and reactive predictions.
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.