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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Book ChapterDOI
StreamKrimp: Detecting Change in Data Streams
Matthijs van Leeuwen,Arno Siebes +1 more
TL;DR: The StreamKrimp algorithm is introduced, which uses the Krimp algorithm to characterise probability distributions with code tables, which partitions the stream into a sequence of substreams and detects changes in the underlying distribution.
Proceedings ArticleDOI
Detecting Recurring and Novel Classes in Concept-Drifting Data Streams
Mohammad M. Masud,Tahseen M. Al-Khateeb,Latifur Khan,Charu C. Aggarwal,Jing Gao,Jiawei Han,Bhavani Thuraisingham +6 more
TL;DR: This paper proposes a more realistic novel class detection technique, which remembers a class and identifies it as "not novel" when it reappears after a long disappearance, and has shown significant reduction in classification error over state-of-the-art stream classification techniques on several benchmark data streams.
Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach
TL;DR: A multistrategy methodology for conceptual data exploration, which combines machine learning, database and knowledge-based technologies, and demonstrates a high potential utility of the methodology for assisting a user in solving practical data mining and knowledge discovery tasks.
Journal ArticleDOI
An online learning approach to eliminate Bus Bunching in real-time
TL;DR: An automatic control framework to mitigate the Bus Bunching phenomenon in real-time is presented and can potentially reduce bunching by 68% and decrease average passenger waiting times by 4.5%, without prolonging in-vehicle times.
Journal ArticleDOI
Efficient instance-based learning on data streams
Jürgen Beringer,Eyke Hüllermeier +1 more
TL;DR: This paper considers the problem of classification on data streams and develops an instance-based learning algorithm for that purpose and suggests that this algorithm has a number of desirable properties that are not, at least not as a whole, shared by currently existing alternatives.
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.