A Self-Labeling Method for Adaptive Machine Learning by Interactive Causality

Learning from unlabeled data, or self-learning, can substantially reduce the complexity of machine learning (ML) utilization in real-time deployment. While the development of un/ semi-supervised algorithms shows promising results in learning with reduced labels, the fundamental assumption of data smoothness restricts its scope of application, especially with non-stationary data distributions in different domains. Leveraging trans-domain invariant causal relationships, causality has recently been employed to foster robust ML. In this paper, we have developed a generic method for self-labeling data that relies on known causality among interactive objects and learned temporal relations among causal events for identifying and associating labels and input data. The causal time-interval between asynchronous cause and effect events is studied to achieve self-labeling. We utilize dynamical system theory in a 1- d setting to demonstrate that our proposed method outperforms traditional feature similarity based semi-supervised learning. A computer simulation experiment was conducted to generate high-dimensional data, and the comprehensive results reveal the potential of learning adaptation in dynamic environments to improve ML robustness against shifts in data distribution.