:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Seber, Pedro, Braatz, Richard D.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2402.17120
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improving Performance in Classification Tasks with LCEN and the Weighted Focal Differentiable MCC Loss
by: Seber, Pedro, et al.
Published: (2026)

Predicting O-GlcNAcylation Sites in Mammalian Proteins with Transformers and RNNs Trained with a New Loss Function
by: Seber, Pedro
Published: (2024)

Interpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Features
by: Schaeffer, Joachim, et al.
Published: (2024)

Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
by: Oosterhuis, Harrie, et al.
Published: (2024)

Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More
by: Schaeffer, Joachim, et al.
Published: (2024)

A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning
by: Cao, Chunxu, et al.
Published: (2024)

Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
by: Kilic, Afra, et al.
Published: (2025)

Selecting Interpretability Techniques for Healthcare Machine Learning models
by: Sierra-Botero, Daniel, et al.
Published: (2024)

Interpretable Prediction and Feature Selection for Survival Analysis
by: Van Ness, Mike, et al.
Published: (2024)

Causal Feature Selection for Responsible Machine Learning
by: Moraffah, Raha, et al.
Published: (2024)

Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning
by: Bertsimas, Dimitris, et al.
Published: (2024)

Learning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Charging
by: Hirt, Sebastian, et al.
Published: (2024)

Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection
by: Wu, Xingyu, et al.
Published: (2024)

Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets
by: Raymond, Matt, et al.
Published: (2024)

Evaluating Fair Feature Selection in Machine Learning for Healthcare
by: Zawad, Md Rahat Shahriar, et al.
Published: (2024)

Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution
by: Hinterleitner, Alexander, et al.
Published: (2024)

A Comparative Study of Machine Learning Algorithms for Electricity Price Forecasting with LIME-Based Interpretability
by: Zhao, Xuanyi, et al.
Published: (2025)

Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions
by: Kim, Minkyu, et al.
Published: (2022)

Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging
by: Hirt, Sebastian, et al.
Published: (2024)

Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach
by: Samarasinghage, Chanuka Don, et al.
Published: (2025)

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection
by: Gill, Naveen, et al.
Published: (2026)

Stroke Disease Classification Using Machine Learning with Feature Selection Techniques
by: Hasan, Mahade, et al.
Published: (2025)

Cost-sensitive Feature Selection for Support Vector Machines
by: Benítez-Peña, Sandra, et al.
Published: (2024)

Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering
by: Pomeroy, Chloe, et al.
Published: (2025)

Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
by: Nichani, Eshaan, et al.
Published: (2023)

Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation
by: Rhyu, Jinwook, et al.
Published: (2024)

Bayesian Optimization for Simultaneous Selection of Machine Learning Algorithms and Hyperparameters on Shared Latent Space
by: Ishikawa, Kazuki, et al.
Published: (2025)

A Copula Based Supervised Filter for Feature Selection in Diabetes Risk Prediction Using Machine Learning
by: Aich, Agnideep, et al.
Published: (2025)

Feature-Selective Representation Misdirection for Machine Unlearning
by: Chen, Taozhao, et al.
Published: (2025)

A Framework for Interpretability in Machine Learning for Medical Imaging
by: Wang, Alan Q., et al.
Published: (2023)

DeepSelective: Interpretable Prognosis Prediction via Feature Selection and Compression in EHR Data
by: Zhang, Ruochi, et al.
Published: (2025)

Partial Information Decomposition for Data Interpretability and Feature Selection
by: Westphal, Charles, et al.
Published: (2024)

ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning
by: Ho, Yu-Hsuan, et al.
Published: (2023)

Dual Interpretation of Machine Learning Forecasts
by: Coulombe, Philippe Goulet, et al.
Published: (2024)

Interpretable Machine Learning for Survival Analysis
by: Langbein, Sophie Hanna, et al.
Published: (2024)

Probabilistic Scoring Lists for Interpretable Machine Learning
by: Hanselle, Jonas, et al.
Published: (2024)

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection
by: Salles, Isadora, et al.
Published: (2024)

Unsupervised Feature Selection Algorithm Based on Dual Manifold Re-ranking
by: Liang, Yunhui, et al.
Published: (2024)

Unsupervised Feature Selection Algorithm Based on Graph Filtering and Self-representation
by: Liang, Yunhui, et al.
Published: (2024)

Algorithm for Interpretable Graph Features via Motivic Persistent Cohomology
by: Maruyama, Yoshihiro
Published: (2025)