Interpretable machine learning aims at unveiling the reasons behind predictions returned by
uninterpretable classifiers. One of the most valuable types of explanation consists of …

Ensemble multifeatured deep learning methodology has emerged as a powerful approach
to overcome the limitations of single deep learning models in terms of generalization …

The different sets of regulations existing for differ-ent agencies within the government make
the task of creating AI enabled solutions in government dif-ficult. Regulatory restrictions …

Visual analytics enables the coupling of machine learning models and humans in a tightly
integrated workflow, addressing various analysis tasks. Each task poses distinct demands to …

Random Forests and related tree-based methods are popular for supervised learning from
table based data. Apart from their ease of parallelization, their classification performance is …

This paper examines the reproducibility of learned explanations for black-box predictions via
model distillation using classification trees. We find that common tree distillation methods fail …

Organizations execute decisions within business processes on a daily basis whilst having to
take into account multiple stakeholders who might require multiple point of views of the …

Randomized experimentation (also known as A/B testing or bucket testing) is widely used in
the internet industry to measure the metric impact obtained by different treatment variants …

Big data mining is one of the major challenging research issues in the field of machine
learning for data mining applications in this present digital era. Big data consists of 3V's:(1) …

Decision tree classifiers have been proved to be among the most interpretable models due
to their intuitive structure that illustrates decision processes in form of logical rules …