Communications of the ACM - Special issue on parallelism
Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
Case-based reasoning
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
Unifying instance-based and rule-based induction
Machine Learning
Tolerating Concept and Sampling Shift in Lazy Learning UsingPrediction Error Context Switching
Artificial Intelligence Review - Special issue on lazy learning
Lazy learning
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
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Incremental Induction of Decision Trees
Machine Learning
Incremental Learning from Noisy Data
Machine Learning
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
XXL - A Library Approach to Supporting Efficient Implementations of Advanced Database Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Estimating Rarity and Similarity over Data Stream Windows
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Issues in data stream management
ACM SIGMOD Record
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Approximate join processing over data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering decision rules from numerical data streams
Proceedings of the 2004 ACM symposium on Applied computing
Cost-efficient mining techniques for data streams
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Incremental learning with partial instance memory
Artificial Intelligence
Incremental rule learning based on example nearness from numerical data streams
Proceedings of the 2005 ACM symposium on Applied computing
Learning decision trees from dynamic data streams
Proceedings of the 2005 ACM symposium on Applied computing
ACM SIGMOD Record
Data streams classification by incremental rule learning with parameterized generalization
Proceedings of the 2006 ACM symposium on Applied computing
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Efficient Reservoir Sampling for Transactional Data Streams
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Adaptive, hands-off stream mining
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Rule induction and instance-based learning a unified approach
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An adaptive nearest neighbor classification algorithm for data streams
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Combining Time and Space Similarity for Small Size Learning under Concept Drift
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Data compression by volume prototypes for streaming data
Pattern Recognition
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
Applied Soft Computing
Classification model for data streams based on similarity
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Batch-incremental versus instance-incremental learning in dynamic and evolving data
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
AIB2: an abstraction data reduction technique based on IB2
Proceedings of the 6th Balkan Conference in Informatics
A similarity-based approach for data stream classification
Expert Systems with Applications: An International Journal
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The processing of data streams in general and the mining of such streams in particular have recently attracted considerable attention in various research fields. A key problem in stream mining is to extend existing machine learning and data mining methods so as to meet the increased requirements imposed by the data stream scenario, including the ability to analyze incoming data in an online, incremental manner, to observe tight time and memory constraints, and to appropriately respond to changes of the data characteristics and underlying distributions, amongst others. This paper considers the problem of classification on data streams and develops an instance-based learning algorithm for that purpose. The experimental studies presented in the paper suggest that this algorithm has a number of desirable properties that are not, at least not as a whole, shared by currently existing alternatives. Notably, our method is very flexible and thus able to adapt to an evolving environment quickly, a point of utmost importance in the data stream context. At the same time, the algorithm is relatively robust and thus applicable to streams with different characteristics.