The study of planetary habitability has gained widespread attention, and most existing studies focused on analyzing the habitability of a single planet based on a single feature, which makes it difficult to process a large amount of planetary data quickly. In this paper, we propose a machine learning-based identification method for efficiently distinguishing the habitability of a batch of planets. Firstly, a planet dataset comprising 5476 unlabeled records from the NASA Exoplanet Archive and 63 labeled entries with habitability from the Habitable Worlds Catalog is collected. Following that, a binary particle swarm optimization approach is used to select the most relevant features according to the 63 labeled planets. To address the missing values in the NASA data, next a standardized median imputation technique is applied. Two distinct methods, namely K-means clustering and distance-based filtering, are developed to label a subset of uninhabitable exoplanets by integrating the unlabeled 5476 and labeled 63 data points. Finally, KNN classifier and a semi-supervised label spreading classifier are trained and cooperated, contributing to the accomplishment of the final classification task. The experimental outcomes demonstrate the viability and effectiveness of the proposed method.
Xu et al. (Sat,) studied this question.