A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.

Python Meta-Feature Extractor package.

Meta-Feature Extractor

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.

MfeatExtractor is an automated code for meta-feature extraction, useful for meta-learning projects.

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

Set of meta-features for model selection in anomaly detection tasks based on domain-specific properties

HYPC-Net combines deep convolutional neural networks with classical machine learning techniques to achieve superior accuracy in classifying yoga poses. This project includes a comprehensive analysis of model performance using the Yoga-82 dataset, offering a comparative study against state-of-the-art CNN models.