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
Incremental entropy-based clustering on categorical data streams with concept drift
TL;DR: This paper proposes an integrated framework for clustering categorical data streams with three algorithms: Minimal Dissimilarity Data Labeling (MDDL), Concept Drift Detection (CDD) and Cluster Evolving Analysis (CEA).
Journal ArticleDOI
A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority
TL;DR: Experimental evaluation using various artificial and real-world datasets proves that DDWM provides very high accuracy in classifying new data instances, irrespective of size of dataset, type of drift or presence of noise.
Book ChapterDOI
Tracking Drifting Concepts by Time Window Optimisation
Ivan Koychev,Robert Lothian +1 more
TL;DR: A mechanism that tracks changing concepts using an adaptive time window and uses a significance test to detect concept drift and then optimizes the size of the time window, aiming to maximise the classification accuracy on recent data.
Meta-Learning, Model Selection, and Example Selection in Machine Learning Domains with Concept Drift.
TL;DR: This paper proposes a metalearning approach allowing the use of alternative learners and automatically selecting the most promising base learner at each step in time, and investigates, if such a contextdependent selection of the base learners leads to a better adaptation to the drifting concept.
Posted Content
Online Machine Learning in Big Data Streams.
TL;DR: This article provides an overview of distributed software architectures and libraries as well as machine learning models for online learning, and highlights the most important ideas for classification, regression, recommendation, and unsupervised modeling from streaming data, and shows how they are implemented in various distributed data stream processing systems.
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.