Instance-Based Learning Algorithms
Machine Learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Learning from Noisy Data
Machine Learning
Classification of Customer Call Data in the Presence of Concept Drift and Noise
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
Journal of Field Robotics - Special Issue on LAGR Program, Part II
Designing an expert system for fraud detection in private telecommunications networks
Expert Systems with Applications: An International Journal
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Classification of evolving data stream requires adaptation during exploitation of algorithms to follow the changes in data. One of the approaches to provide the classifier the ability to adapt changes is usage of sliding window - learning on the basis of the newest data samples. Active learning is the paradigm in which algorithm decides on its own which data will be used as training samples; labels of only these samples need to be obtained and delivered as the learning material. This paper will investigate the error of classic sliding window algorithm and its active version, as well as its learning curve after sudden drift occurs. Two novel performance measures will be introduces and some of their features will be highlighted.