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
Balanced Efficient Lifelong Learning (B-ELLA) for Cyber Attack Detection
TL;DR: This paper outlines and proposes a new approach to cyber attack detection on the basis of the practical application of the efficient lifelong learning cybersecurity system, an extension of the Efficient Lifelong Learning (ELLA) framework.
Journal ArticleDOI
An Approach For Concept Drift Detection in a Graph Stream Using Discriminative Subgraphs
Ramesh Paudel,William Eberle +1 more
TL;DR: This work proposes a novel unsupervised concept-drift detection method on graph streams called Discriminative Subgraph-based Drift Detector (DSDD), which starts by discovering discriminative subgraphs for each graph in the stream.
Journal ArticleDOI
SCR: simulated concept recurrence – a non‐supervised tool for dealing with shifting concept
Piotr Sobolewski,Michał Woźniak +1 more
TL;DR: This article presents a semi‐supervised method and analyzes the possibilities of using the simulated concept recurrence against concept drift and also expanding the previously presented functionality of the algorithm from the sole concept characterization to both concept drift detection and concept characterization.
Journal ArticleDOI
Classification of Peer-to-Peer Traffic Using A Two-Stage Window-Based Classifier With Fast Decision Tree and IP Layer Attributes
Bijan Raahemi,Ali Mumtaz +1 more
TL;DR: This paper presents a new approach using data mining techniques, and in particular a two-stage architecture, for classification of Peer-to-Peer P2P traffic in IP networks where in the first stage the traffic is filtered using standard port numbers and layer 4 port matching to label well-known P 2P and NonP2P Traffic.
DissertationDOI
Learning Comprehensible Theories from Structured Data
TL;DR: This thesis is concerned with the problem of learning comprehensible theories from structured data and covers primarily classification and regression learning and the usefulness of the learning system developed is demontrated with applications in two important domains: bioinformatics and intelligent agents.
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.