There are 2 repositories under meta-features topic.
A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.
Repository to track the progress in Meta-Learning (MtL), including the datasets and the current state-of-the-art for the most common MtL problems.
The Python Class Overlap Libray (pycol) assembles a comprehensive set of complexity measures associated with the characterization of the Class Overlap problem.
Presented at the 2022 IEEE Region 10 Conference (TENCON 2022). Our main contribution is twofold: (1) the construction of a meta-learning model for recommending a distance metric for k-means clustering and (2) a fine-grained analysis of the importance and effects of the meta-features on the model's output
MfeatExtractor is an automated code for meta-feature extraction, useful for meta-learning projects.
Set of meta-features for model selection in anomaly detection tasks based on domain-specific properties