Semantic classification of spacecraft's status: integrating system intelligence and human knowledge

In this paper, we introduce a novel approach where the system involves human knowledge in the classification task using decision trees. Machine learning techniques are now applied to a variety of tasks in real-world problems. The computer performs complex computations better than humans. However, in many real-world applications, humans have background domain knowledge about the problem that the computer often does not have. For instance, in a spacecraft status classification task, humans have a sense for which factors are likely to correlate with the classes of interest. Without this knowledge, machines may overfit to training data. We propose to combine two models: one based on human reasoning, common sense, or heuristics, and the other learned by a machine learning algorithm in a data-driven manner. In our experiments, we use decision trees and categorical features so that the model consists of rules which are semantic and interpretable for humans. Our proposed approach results in an improvement in classification performance over either models alone. Our work illustrates the possibility of integrating human knowledge and artificial intelligence.