Characteristic rules have been advocated for their ability to improve interpretability over
discriminative rules within the area of rule learning. However, the former type of rule has not …

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

In manufacturing, defect detection is typically performed manually to ensure the required
quality of the produced parts; however, this is a labour-intensive and time-consuming …

Thanks to their theoretically proven properties, Shapley values have received a lot of
attention as a means to explain predictions within the area of explainable machine learning …

Score-based explainable machine-learning techniques are often used to understand the
logic behind black-box models. However, such explanation techniques are often …

Rules output by explainable machine learning techniques naturally come with a degree of
uncertainty, as the complex functionality of the underlying black-box model often can be …

One of the main challenges in using machine learning (ML) models is to ensure the
interpretability of their predictions. Addressing this challenge becomes increasingly …

This study proposes the analysis model using HMM that can estimate the learning state
hidden from observations in a continuous sequence in order to reveal the prerequisite …

A random forest prediction can be computed by the scalar product of the labels of the
training examples and a set of weights that are determined by the leafs of the forest into …

Shapley values have multiple desired and theoretically proven properties for explaining
black-box model predictions. However, the exact computation of Shapley values can be …