Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Space-efficient online computation of quantile summaries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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
Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Machine Learning
Continuous queries over data streams
ACM SIGMOD Record
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Is random model better? On its accuracy and efficiency
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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
On the optimality of probability estimation by random decision trees
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Tracking concept drifting with an online-optimized incremental learning framework
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
System approach to intrusion detection using hidden Markov model
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Recency-based collaborative filtering
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
Distributed and control theoretic approach to intrusion detection
IWCMC '07 Proceedings of the 2007 international conference on Wireless communications and mobile computing
Using classifier ensembles to label spatially disjoint data
Information Fusion
Dynamic integration of classifiers for handling concept drift
Information Fusion
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
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
An active learning system for mining time-changing data streams
Intelligent Data Analysis
Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners
The Journal of Machine Learning Research
Knowledge transfer via multiple model local structure mapping
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
Peer to peer botnet detection for cyber-security: a data mining approach
Proceedings of the 4th annual workshop on Cyber security and information intelligence research: developing strategies to meet the cyber security and information intelligence challenges ahead
Classifying Evolving Data Streams Using Dynamic Streaming Random Forests
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Mining decision rules on data streams in the presence of concept drifts
Expert Systems with Applications: An International Journal
An adaptive personalized news dissemination system
Journal of Intelligent Information Systems
A Multi-partition Multi-chunk Ensemble Technique to Classify Concept-Drifting Data Streams
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and 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
A framework for flexible clustering of multiple evolving data streams
International Journal of Advanced Intelligence Paradigms
Data Mining and Knowledge Discovery
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
Lacking Labels in the Stream: Classifying Evolving Stream Data with Few Labels
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Enhancing recommender systems under volatile userinterest drifts
Proceedings of the 18th ACM conference on Information and knowledge management
Dynamic security policy learning
Proceedings of the first ACM workshop on Information security governance
Statistical Instance-Based Ensemble Pruning for Multi-class Problems
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Quick adaptation to changing concepts by sensitive detection
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Online evolutionary collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Adapting neighborhood and matrix factorization models for context aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Classification and novel class detection of data streams in a dynamic feature space
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Robust ensemble learning for mining noisy data streams
Decision Support Systems
Detecting and ordering salient regions
Data Mining and Knowledge Discovery
Editorial: Classifying text streams by keywords using classifier ensemble
Data & Knowledge Engineering
Cloud-based malware detection for evolving data streams
ACM Transactions on Management Information Systems (TMIS)
Accuracy updated ensemble for data streams with concept drift
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?
Expert Systems with Applications: An International Journal
Classifying noisy data streams
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Learning with local drift detection
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Improving the performance of data stream classifiers by mining recurring contexts
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Temporal evolution and local patterns
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Establishing fraud detection patterns based on signatures
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Detecting change via competence model
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
A new method of mining data streams using harmony search
Journal of Intelligent Information Systems
Recentness biased learning for time series forecasting
Information Sciences: an International Journal
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One major problem of existing methods to mine data streams is that it makes ad hoc choices to combine most recent data with some amount of old data to search the new hypothesis. The assumption is that the additional old data always helps produce a more accurate hypothesis than using the most recent data only. We first criticize this notion and point out that using old data blindly is not better than "gambling"; in other words, it helps increase the accuracy only if we are "lucky." We discuss and analyze the situations where old data will help and what kind of old data will help. The practical problem on choosing the right example from old data is due to the formidable cost to compare different possibilities and models. This problem will go away if we have an algorithm that is extremely efficient to compare all sensible choices with little extra cost. Based on this observation, we propose a simple, efficient and accurate cross-validation decision tree ensemble method.