Bottom-Up and Top-Down Workflows for Hypercube- And Clustering-Based Knowledge Extractors

Federico Sabbatini, Roberta Calegari

Abstract: Machine learning opaque models, currently exploited to carry out a wide variety of supervised and unsupervised learning tasks, are able to achieve impressive predictive performances. However, they act as black boxes (BBs) from the human standpoint, so they cannot be entirely trusted in critical applications unless there exists a method to extract symbolic and human-readable knowledge out of them.