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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Proceedings ArticleDOI
Anomaly detection based on eccentricity analysis
TL;DR: This paper proposes a new less conservative and more sensitive condition for anomaly detection, quite different from the traditional “nσ” type conditions, and points to some possible applications which will be the domain of future work.
Journal ArticleDOI
Dynamic churn prediction framework with more effective use of rare event data: The case of private banking
Özden Gür Ali,Umut Arıtürk +1 more
TL;DR: A dynamic churn prediction framework for generating training data from customer records is proposed, and it significantly increases the prediction accuracy across prediction horizons compared to the standard approach of one observation per customer.
Journal ArticleDOI
Adaptive Clustering for Dynamic IoT Data Streams
TL;DR: This work proposes a method which determines how many different clusters can be found in a stream based on the data distribution, and demonstrates how the number of clusters in a real-world data stream can be determined by analyzing the data distributions.
Journal ArticleDOI
Exploiting Class Hierarchies for Knowledge Transfer in Hyperspectral Data
TL;DR: A knowledge transfer framework that leverages the information extracted from the existing labeled data to classify spatially separate and multitemporal test data is proposed and shows that in the absence of any labeled data in the new area, the approach is better than a direct application of the original classifier on the new data.
Journal ArticleDOI
Real-time data mining of non-stationary data streams from sensor networks
TL;DR: A new real-time data-mining algorithm called IOLIN (incremental on-line information network), which saves a significant amount of computational effort by updating an existing model as long as no major concept drift is detected.
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.