Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

Machine learning (ML) has the potential to transform oncology and, more broadly, medicine.
1 The introduction of ML in health care has been enabled by the digitization of patient data …

Many clustering algorithms lead to cluster assignments that are hard to explain, partially
because they depend on all the features of the data in a complicated way. To improve …

State-of-the-art clustering algorithms provide little insight into the rationale for cluster
membership, limiting their interpretability. In complex real-world applications, the latter …

In recent years, black-box models have developed rapidly because of their high accuracy.
Balancing the interpretability and accuracy is increasingly important. The lack of …

In clustering process, fuzzy partition performs better than hard partition when the boundaries
between clusters are vague. Whereas, traditional fuzzy clustering algorithms produce less …

A number of recent works have employed decision trees for the construction of explainable
partitions that aim to minimize the k-means cost function. These works, however, largely …

Despite the popularity of explainable AI, there is limited work on effective methods for
unsupervised learning. We study algorithms for $ k $-means clustering, focusing on a trade …

A recent trend in machine learning has been to enrich learned models with the ability to
explain their own predictions. The emerging field of explainable AI (XAI) has so far mainly …

A distribution shift can have fundamental consequences such as signaling a change in the
operating environment or significantly reducing the accuracy of downstream models. Thus …