COLT '90 Proceedings of the third annual workshop on Computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Journal of the ACM (JACM)
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Data selection for support vector machine classifiers
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive disk spin—down for mobile computers
Mobile Networks and Applications
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Experimental comparisons of online and batch versions of bagging and boosting
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
Large Scale Kernel Regression via Linear Programming
Machine Learning
The Relaxed Online Maximum Margin Algorithm
Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
On-line Algorithms in Machine Learning
Developments from a June 1996 seminar on Online algorithms: the state of the art
Tracking a small set of experts by mixing past posteriors
The Journal of Machine Learning Research
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Learning decision trees from dynamic data streams
Proceedings of the 2005 ACM symposium on Applied computing
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning to rank networked entities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
Data Mining and Knowledge Discovery
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Predicting electricity distribution feeder failures using machine learning susceptibility analysis
IAAI'06 Proceedings of the 18th conference on Innovative applications of artificial intelligence - Volume 2
Change detection with kalman filter and CUSUM
DS'06 Proceedings of the 9th international conference on Discovery Science
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Learning with local drift detection
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Parameterizing random test data according to equivalence classes
Proceedings of the 2nd international workshop on Random testing: co-located with the 22nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2007)
Adaptive methods for classification in arbitrarily imbalanced and drifting data streams
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Proceedings of the First International Workshop on Data Mining for Service and Maintenance
| Hi-index | 0.00 |
In many practical applications, one is interested in generating a ranked list of items using information mined from continuous streams of data. For example, in the context of computer networks, one might want to generate lists of nodes ranked according to their susceptibility to attack. In addition, real-world data streams often exhibit concept drift, making the learning task even more challenging. We present an online learning approach to ranking with concept drift, using weighted majority techniques. By continuously modeling different snapshots of the data and tuning our measure of belief in these models over time, we capture changes in the underlying concept and adapt our predictions accordingly. We measure the performance of our algorithm on real electricity data as well as asynthetic data stream, and demonstrate that our approach to ranking from stream data outperforms previously known batch-learning methods and other online methods that do not account for concept drift.