An ensemble-based online learning algorithm for streaming data
This is an incremental improvement for applications requiring real-time processing of streaming data.
The authors tackled the problem of online learning for streaming data by proposing an ensemble-based approach using Naive Bayes classifiers on lower-dimensional projections, and the result showed it performed significantly better than well-known online learning algorithms on UCI and synthetic datasets.
In this study, we introduce an ensemble-based approach for online machine learning. The ensemble of base classifiers in our approach is obtained by learning Naive Bayes classifiers on different training sets which are generated by projecting the original training set to lower dimensional space. We propose a mechanism to learn sequences of data using data chunks paradigm. The experiments conducted on a number of UCI datasets and one synthetic dataset demonstrate that the proposed approach performs significantly better than some well-known online learning algorithms.