IEEE

Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception

Abstract: Common approaches to explainable AI (XAI) for deep learning focus on analyzing the importance of input features on the classification task in a given model: saliency methods like SHAP and ...
GitHub

A comprehensive Python package for loading, processing, and working with datasets used in fair classification.

FairML Datasets provides tools and interfaces to download, load, transform, and analyze the datasets in the FairGround corpus. It handles sensitive attributes and facilitates fairness-aware machine ...
Hosted on MSN

Sleep Stage Classification with Python: EEG, Scikit-Learn & MNE

Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!
IEEE

Feature Importance Guided Network With Missing Value Learning for Pneumonia Pathogen Classification and Biomarker Discovery

Abstract: Discovering clinical biomarkers through multivariate modeling is crucial for precise pneumonia diagnosis and treatment. However, existing methods often fall short in either their ...
Scientific Research Publishing

Sovann, C., Olin, S., Mansourian, A., Sakhoeun, S., Prey, S., Kok, S. et al. (2025). Importance of Spectral Information, Seasonality, and Topography on Land Cover ...

ABSTRACT: This paper proposes a GIS-based approach to classifying land cover using key morphometric indicators—slope, aspect, and elevation. The study focuses on the Chepelarska River basin in the ...
Scientific Research Publishing

Williamson, B.D., Gilbert, P.B., Carone, M. and Simon, N. (2020) Nonparametric Variable Importance Assessment Using Machine Learning Techniques. Biometrics, 77, 9-22.

ABSTRACT: We introduce the Kernel-based Partial Conditional Mean Dependence, a scalar-valued measure of conditional mean dependence of Y given X , while adjusting for the nonlinear dependence on Z .