Decision Tree
Decision trees are a fundamental machine learning model aiming to create interpretable predictive models by recursively partitioning data based on feature values. Current research emphasizes improving their accuracy, efficiency, and robustness, particularly within ensemble methods like random forests and gradient boosted trees, as well as exploring novel architectures like auto-regressive decision trees and their application in areas such as natural language processing. This ongoing work addresses challenges like vulnerability to adversarial attacks, handling noisy data, and optimizing for both accuracy and interpretability, impacting diverse fields from fraud detection to cybersecurity and beyond.
Papers
Comparison of decision trees with Local Interpretable Model-Agnostic Explanations (LIME) technique and multi-linear regression for explaining support vector regression model in terms of root mean square error (RMSE) values
Amit Thombre
Register Your Forests: Decision Tree Ensemble Optimization by Explicit CPU Register Allocation
Daniel Biebert, Christian Hakert, Kuan-Hsun Chen, Jian-Jia Chen
Finding Decision Tree Splits in Streaming and Massively Parallel Models
Huy Pham, Hoang Ta, Hoa T. Vu
Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach
José Bobes-Bascarán, Eduardo Mosqueira-Rey, Ángel Fernández-Leal, Elena Hernández-Pereira, David Alonso-Ríos, Vicente Moret-Bonillo, Israel Figueirido-Arnoso, Yolanda Vidal-Ínsua