Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning in the presence of concept drift and hidden contexts
Machine Learning
Error reduction through learning multiple descriptions
Machine Learning
Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Continuous queries over data streams
ACM SIGMOD Record
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
An Adaptive Learning Approach for Noisy Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
Introduction To Business Data Mining
Introduction To Business 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 Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A logical framework for identifying quality knowledge from different data sources
Decision Support Systems
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A fast algorithm for finding frequent episodes in event streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and 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
Classification algorithm sensitivity to training data with non representative attribute noise
Decision Support Systems
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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
Answering linear optimization queries with an approximate stream index
Knowledge and Information Systems
On classification and segmentation of massive audio data streams
Knowledge and Information Systems
A hybrid approach for efficient ensembles
Decision Support Systems
Mining Data Streams with Labeled and Unlabeled Training Examples
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Active learning from stream data using optimal weight classifier ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Classifying noisy data streams
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Enabling fast prediction for ensemble models on data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A Utility-Based Recommendation Approach for Academic Literatures
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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
Mining frequent patterns across multiple data streams
Proceedings of the 20th ACM international conference on Information and knowledge management
Continuous data stream query in the cloud
Proceedings of the 20th ACM international conference on Information and knowledge management
CLAP: Collaborative pattern mining for distributed information systems
Decision Support Systems
Group detection and relation analysis research for web social network
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Inverse matrix-free incremental proximal support vector machine
Decision Support Systems
A similarity-based approach for data stream classification
Expert Systems with Applications: An International Journal
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In this paper, we study the problem of learning from concept drifting data streams with noise, where samples in a data stream may be mislabeled or contain erroneous values. Our essential goal is to build a robust prediction model from noisy stream data to accurately predict future samples. For noisy data sources, most existing works rely on data preprocessing techniques to cleanse noisy samples before the training of decision models. In data stream environments, these data preprocessing techniques are, unfortunately, hard to apply, mainly because the concept drifting in a data stream may make it very difficult to differentiate noise from samples of changing concepts. Accordingly, we propose an aggregate ensemble (AE) learning framework. The aim of AE is to build a robust ensemble model that can tolerate data errors. Theoretical and empirical studies on both synthetic and real-world data streams demonstrate that the proposed AE learning framework is capable of building accurate classification models from noisy data streams.