University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Online Ensemble Learning of Data Streams with Gradually Evolved Classes

Online Ensemble Learning of Data Streams with Gradually Evolved Classes

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If you have a question about this talk, please contact Hector Basevi.

Host: Prof Peter Tino

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In machine learning, class evolution is the phenomenon of class emergence and disappearance. It is likely to occur in many data stream problems, which are problems where additional training data become available over time. For example, in the problem of classifying tweets according to their topic, new topics may emerge over time, and certain topics may become unpopular and not discussed anymore. Therefore, class evolution is an important research topic in the area of learning data streams. Existing work implicitly regards class evolution as an abrupt change. However, in many real world problems, classes emerge or disappear gradually. This gives rise to extra challenges, such as non-stationary imbalance ratios between the different classes in the problem. In this talk, I will present an ensemble approach able to deal with gradually evolved classes. In order to quickly adjust to class evolution, the ensemble maintains a base learner for each class and dynamically creates, updates and (de)activates base learners whenever new training data become available. It also uses a dynamic undersampling technique in order to deal with the non-stationary class imbalance present in this type of problem. Empirical studies demonstrate the effectiveness of the proposed approach in various class evolution scenarios in comparison with existing class evolution approaches.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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