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
Just-In-Time Classifiers for Recurrent Concepts
TL;DR: A novel generation of JIT classifiers able to deal with recurrent concept drift is presented by means of a practical formalization of the concept representation and the definition of a set of operators working on such representations.
Book ChapterDOI
Fast and Light Boosting for Adaptive Mining of Data Streams
Fang Chu,Carlo Zaniolo +1 more
TL;DR: A novel boosting ensemble method based on a dynamic sample-weight assignment scheme that achieves the accuracy of traditional boosting without requiring multiple passes through the data, and which assures faster learning and competitive accuracy using simpler base models.
Proceedings Article
Adaptive concept drift detection
Anton Dries,Ulrich Rückert +1 more
TL;DR: In this paper, the authors present three novel drift detection tests, whose test statistics are dynamically adapted to match the actual data at hand, based on a rank statistic on density estimates for a binary representation of the data, the second compares average margins of a linear classifier induced by the 1norm support vector machine (SVM), and the last one is based on the average zero-one, sigmoid or stepwise linear error rate of an SVM classifier.
Journal ArticleDOI
Scalable and efficient multi-label classification for evolving data streams
TL;DR: This paper proposes a new experimental framework for learning and evaluating on multi-label data streams, and uses it to study the performance of various methods, and develops a multi- Label Hoeffding tree with multi- label classifiers at the leaves.
A Case-Based Approach to Spam Filtering that Can Track Concept Drift
TL;DR: A case-based approach to spam filtering allows for the sharing of cases and thus a sharing of the effort of labeling email as spam.
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.
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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.
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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.