On-line learning and stochastic approximations
On-line learning in neural networks
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
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
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Efficient instance-based learning on data streams
Intelligent Data Analysis
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Learning from Evolving Data Streams
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
The Journal of Machine Learning Research
Leveraging bagging for evolving data streams
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Enabling Fast Lazy Learning for Data Streams
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Fast perceptron decision tree learning from evolving data streams
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Dealing with concept drift and class imbalance in multi-label stream classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Efficient data stream classification via probabilistic adaptive windows
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Many real world problems involve the challenging context of data streams, where classifiers must be incremental: able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Two approaches dominate the literature: batch-incremental methods that gather examples in batches to train models; and instance-incremental methods that learn from each example as it arrives. Typically, papers in the literature choose one of these approaches, but provide insufficient evidence or references to justify their choice. We provide a first in-depth analysis comparing both approaches, including how they adapt to concept drift, and an extensive empirical study to compare several different versions of each approach. Our results reveal the respective advantages and disadvantages of the methods, which we discuss in detail.