Ensemble Framework for Missing Feature Problem in Data Stream Classification

Hanqing Hu, Mehmed Kantardzic

A dynamic data stream requires the classification framework to adapt to changes in the stream. A common strategy for adaptation is to train new models or update existing models when changes occur. However, in real world applications, some features of the data can be missing when training new models. This can be due to faulty devices or interruption in data transmission. The performance for new models trained with incomplete data may be negatively impacted. If no update to models occurs, performance may remain low even after the data stream is restored back to full feature. To solve this missing feature problem we propose Ensemble Framework for Missing Feature (EFMF). The framework trains new models using available features then update the model once the data stream is restored. Experimentally we show that our framework outperforms the two naïve approaches where the framework waits for all features and then trains new models and where the framework train with incomplete data with no update later on.

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