Implicit Context-aware Learning and Discovery for Streaming Data Analytics

TL;DR

This paper introduces an online-learning approach using neural autoencoders to detect and adapt to changing contexts in streaming data, improving classifier training efficiency during contextual shifts.

Contribution

It presents a novel neural autoencoder-based method for real-time context detection in streaming data, enhancing online learning adaptability.

Findings

Quicker convergence during contextual changes

Effective automatic detection of context shifts

Improved classifier performance in streaming scenarios

Abstract

The performance of machine learning model can be further improved if contextual cues are provided as input along with base features that are directly related to an inference task. In offline learning, one can inspect historical training data to identify contextual clusters either through feature clustering, or hand-crafting additional features to describe a context. While offline training enjoys the privilege of learning reliable models based on already-defined contextual features, online training for streaming data may be more challenging -- the data is streamed through time, and the underlying context during a data generation process may change. Furthermore, the problem is exacerbated when the number of possible context is not known. In this study, we propose an online-learning algorithm involving the use of a neural network-based autoencoder to identify contextual changes during training, then compares the currently-inferred context to a knowledge base of learned contexts as training advances. Results show that classifier-training benefits from the automatically discovered contexts which demonstrates quicker learning convergence during contextual changes compared to current methods.