Learning-to-learn efficiently with self-learning

Abstract

Digital Twins of industrial process plants enable various what-if and if-what scenarios of the plants' functioning for fault diagnosis and general monitoring in the real-world. They do so through machine learning (ML) models built using data from sensors fitted in the plant. Over time, environmental factors cause variations in sensor readings, adversely affecting quality of the models' predictions. This triggers the self-learning loop, leading to the re-tuning/re-training of models. Reducing the time spent in self-learning of the models is a challenging task since there exist multiple models that need to be trained repeatedly using multiple algorithms which translates into large training time. We propose a metalearner which recommends the optimal regression algorithm for a model, thereby eliminating the need for training the model on multiple algorithms for every self-learning instance. The metalearner is trained on metafeatures extracted from the data which makes it application agnostic. We introduce domain metafeatures, which enhance metalearner prediction accuracy and propose machine learning and deep learning based approaches for selecting optimal metafeatures. To ensure relevance of selected metafeatures, we introduce novel static and dynamic reward functions for dynamic metafeature selection using a Q-Learning based approach. Our metalearning approach accelerates the time for determining the optimal regressor among 5 potential regressors from 5X to 27X over the traditional self-learning approaches. The incremental pre-processing approach achieves a speed-up of 25X over the traditional approach. The proposed metalearner achieves an AUC of 0.989, 0.954 and 0.998 for ML, DL and RL based approaches for metafeature selection respectively. We illustrate our findings on 3 datasets from the industrial process domain.