An ensemble-based online learning algorithm for streaming data

2017/04/26 by Tien Thanh Nguyen, Nguyen, Tien Thanh, Nguyen, Thi Thu Thuy +6
Computer Science · #Data Stream Mining Techniques #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.1704.07938

openalex publication_date 2017/04/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• The perceptron: A probabilistic model for information storage and organization in the brain.
• A systematic analysis of performance measures for classification tasks

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