Machine Learning and Data Classification

Leveraging Permutation Testing to Assess Confidence in Positive-Unlabeled Learning Applied to High-Dimensional Biological Datasets

This paper introduces a permutation testing approach to evaluate PU learning models using KP samples without ground truth labels, showing its effectiveness on synthetic and real datasets.

Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis

MLme simplifies machine learning for researchers by integrating data exploration, AutoML, and visualization, showing promising performance across datasets and identifying cell population markers.

Tree-weighting for multi-study ensemble learners

This paper develops tree-weighting methods for multi-study ensemble learning, showing improved robustness and feature interpretation in genomic datasets.

Scaling tree-based automated machine learning to biomedical big data with a dataset selector

The paper introduces two features to TPOT, a machine learning tool, to improve its scalability and efficiency for biomedical big data analysis.