Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Relevant Data Expansion for Learning Concept Drift from Sparsely Labeled Data
IEEE Transactions on Knowledge and Data Engineering
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Tackling concept drift by temporal inductive transfer
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Quantifying trends accurately despite classifier error and class imbalance
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Suppressing model overfitting in mining concept-drifting data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Applying lazy learning algorithms to tackle concept drift in spam filtering
Expert Systems with Applications: An International Journal
Transductive Methods for the Distributed Ensemble Classification Problem
Neural Computation
Online classification of nonstationary data streams
Intelligent Data Analysis
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Recursive estimation of prior probabilities using a mixture
IEEE Transactions on Information Theory
Asymptotically efficient estimation of prior probabilities in multiclass finite mixtures
IEEE Transactions on Information Theory
Quantification and semi-supervised classification methods for handling changes in class distribution
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer estimation of evolving class priors in data stream classification
Pattern Recognition
Estimating class proportions in boar semen analysis using the hellinger distance
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
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Online classification is important for real time data sequence classification. Its most challenging problem is that the class priors may vary for non-stationary data sequences. Most of the current online-data-sequence-classification algorithms assume that the class labels of some new-arrived data samples are known and retrain the classifier accordingly. Unfortunately, such assumption is often violated in real applications. But if we were able to estimate the class priors on the test data sequence accurately, we could adjust the classifier without retraining it while preserving a reasonable accuracy. There has been some work on the class priors estimation to classify static data sets using the offline iterative EM algorithm, which has been proved to be quite effective to adjust the classifier. Inspired by the offline iterative EM algorithm for static data sets, in this paper, we propose an online incremental EM algorithm to estimate the class priors along the data sequence. The classifier is adjusted accordingly to keep pace with the varying distribution. The proposed online algorithm is more computationally efficient because it scans the sequence only once. Experimental results show that the proposed algorithm indeed performs better than the conventional offline iterative EM algorithm when the class priors are non-stationary.