Abstract “Black box” models created by modern machine learning techniques are typically
hard to interpret. Thus, the necessity of explainable artificial intelligence (XAI) has grown for …

Abstract Random Forests (RFs) are powerful ensemble learning algorithms that are widely
used in various machine learning tasks. However, they tend to overfit noisy or irrelevant …

Serverless computing has received growing interest in recent years for supporting large-
scale machine learning tasks. However, training a machine learning model in a serverless …

Ensemble methods, such as random forest algorithms, typically outperform single classifiers.
However, they often demand substantial storage memory and involve relatively time …

Abstract Machine learning has become an integral part of everyday life ranging from
applications in AI-powered search queries to (partial) autonomous driving. Many of the …

Abstract We introduce Splitting Stump Forests–small ensembles of weak learners extracted
from a trained random forest. The high memory consumption of random forest ensemble …

As machine learning models become increasingly integrated into various applications, the
need for resource-aware deployment strategies becomes paramount. One promising …

This chapter discusses various enhancement strategies of exploration and exploitation
mechanisms that focus on the perspective of location, size, and capacity associated with the …

Tree Ensemble Compression for Interpretability Page 1 Tree Ensemble Compression for
Interpretability Laurens Devos, Deniz Can Oruç, Wannes Meert, Hendrik Blockeel, and Jesse …