Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery and Data Mining: The Info-Fuzzy Network (Ifn) Methodology
Knowledge Discovery and Data Mining: The Info-Fuzzy Network (Ifn) Methodology
Machine Learning
Mining complex models from arbitrarily large databases in constant time
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Compact and Accurate Model for Classification
IEEE Transactions on Knowledge and Data Engineering
Communications of the ACM - Wireless sensor networks
On demand classification of data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A note on the utility of incremental learning
AI Communications
Online classification of nonstationary data streams
Intelligent Data Analysis
StreamMiner: a classifier ensemble-based engine to mine concept-drifting data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Real-time data mining of non-stationary data streams from sensor networks
Information Fusion
Online hybrid traffic classifier for Peer-to-Peer systems based on network processors
Applied Soft Computing
Concept-based evidential reasoning for multimodal fusion in human-computer interaction
Applied Soft Computing
An Integrated Knowledge Adaption Framework for Case-Based Reasoning Systems
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Neural visualization of network traffic data for intrusion detection
Applied Soft Computing
Increasing availability of industrial systems through data stream mining
Computers and Industrial Engineering
Detecting change via competence model
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Kernel-based selective ensemble learning for streams of trees
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Dynamic rough clustering and its applications
Applied Soft Computing
A fuzzy coherent rule mining algorithm
Applied Soft Computing
Concept drift detection via competence models
Artificial Intelligence
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Most data-mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by dynamic processes, which may change over time, in some cases even drastically. The change in the underlying concept, also known as concept drift, causes the data-mining model generated from past examples to become less accurate and relevant for classifying the current data. Most online learning algorithms deal with concept drift by generating a new model every time a concept drift is detected. On one hand, this solution ensures accurate and relevant models at all times, thus implying an increase in the classification accuracy. On the other hand, this approach suffers from a major drawback, which is the high computational cost of generating new models. The problem is getting worse when a concept drift is detected more frequently and, hence, a compromise in terms of computational effort and accuracy is needed. This work describes a series of incremental algorithms that are shown empirically to produce more accurate classification models than the batch algorithms in the presence of a concept drift while being computationally cheaper than existing incremental methods. The proposed incremental algorithms are based on an advanced decision-tree learning methodology called ''Info-Fuzzy Network'' (IFN), which is capable to induce compact and accurate classification models. The algorithms are evaluated on real-world streams of traffic and intrusion-detection data.