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
A streaming ensemble algorithm (SEA) for large-scale classification
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
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On demand classification of data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
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)
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Active Learning from Data Streams
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
On Appropriate Assumptions to Mine Data Streams: Analysis and Practice
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Model selection under covariate shift
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
An Aggregate Ensemble for Mining Concept Drifting Data Streams with Noise
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Online phishing classification using adversarial data mining and signaling games
Proceedings of the ACM SIGKDD Workshop on CyberSecurity and Intelligence Informatics
Improving Adaptive Bagging Methods for Evolving Data Streams
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Transfer estimation of evolving class priors in data stream classification
Pattern Recognition
Online phishing classification using adversarial data mining and signaling games
ACM SIGKDD Explorations Newsletter
Robust ensemble learning for mining noisy data streams
Decision Support Systems
Experimental study on fighters behaviors mining
Expert Systems with Applications: An International Journal
Effective sentiment stream analysis with self-augmenting training and demand-driven projection
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Subword and spatiotemporal models for identifying actionable information in Haitian Kreyol
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Enabling fast prediction for ensemble models on data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Predictive Data Stream Filtering
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Pattern change discovery between high dimensional data sets
Proceedings of the 20th ACM international conference on Information and knowledge management
Probabilistic user modeling in the presence of drifting concepts
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Modified blame-based noise reduction for concept drift
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model
International Journal of Data Warehousing and Mining
Learning from data streams with only positive and unlabeled data
Journal of Intelligent Information Systems
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Concept drift detection via competence models
Artificial Intelligence
Just-in-time adaptive similarity component analysis in nonstationary environments
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Classifying evolving data streams with partially labeled data
Intelligent Data Analysis
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Mining concept drifting data streams is a defining challenge for data mining research. Recent years have seen a large body of work on detecting changes and building prediction models from stream data, with a vague understanding on the types of the concept drifting and the impact of different types of concept drifting on the mining algorithms. In this paper, we first categorize concept drifting into two scenarios: Loose Concept Drifting (LCD) and Rigorous Concept Drifting (RCD), and then propose solutions to handle each of them separately. For LCD data streams, because concepts in adjacent data chunks are sufficiently close to each other, we apply kernel mean matching (KMM) method to minimize the discrepancy of the data chunks in the kernel space. Such a minimization process will produce weighted instances to build classifier ensemble and handle concept drifting data streams. For RCD data streams, because genuine concepts in adjacent data chunks may randomly and rapidly change, we propose a new Optimal Weights Adjustment (OWA) method to determine the optimum weight values for classifiers trained from the most recent (up-to-date) data chunk, such that those classifiers can form an accurate classifier ensemble to predict instances in the yet-to-come data chunk. Experiments on synthetic and real-world datasets will show that weighted instance approach is preferable when the concept drifting is mainly caused by the changing of the class prior probability; whereas the weighted classifier approach is preferable when the concept drifting is mainly triggered by the changing of the conditional probability.