A Feature Evolution Aware Classification Framework for Streaming Data using Dynamic Autoencoder and Ensembled Learning.

Recent advancements in data mining and knowledge discovery have created numerous research opportunities in streaming data analysis. One critical challenge is developing machine learning models that can efficiently handle changes in features and dynamic concepts, including concept drift, feature drift, and feature evolution. State-of-the-art techniques proposed to address these anomalies in data streams often assume that a constant set of features is available for processing. However, in real-time scenarios, the situation is quite different, as the set of features in a stream may vary over time due to factors such as the disappearance of existing features or the emergence of new ones. The proposed work focuses on handling dynamically evolving features by introducing a novel solution that leverages a Dynamic Autoencoder DAE and ensemble learning. Additionally, adaptive windowing and concept-preserving mechanisms improve the proposed architecture by retaining the concept information from previous data windows. The ensemble technique used in the proposed classification framework demonstrates promising performance in diverse datasets, achieving accuracies of 86%, 94%, and 95% in the Weather, Electricity and Forest Cover Type datasets, respectively. This innovative integration of deep learning and traditional methods effectively addresses various challenges in streaming data analysis.