An Efficient Iterative Algorithm to Explainable Feature Learning

This paper summarizes a doctoral thesis introducing the new iterative approach to explainable feature learning. Features are learned in three steps during each iteration: feature construction, evaluation, and selection. We demonstrated superior performances compared to the state of the art on 13 of 15 test cases and the explainability of the learned feature representation for knowledge discovery.