Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

Real-world machine learning applications need to be thoroughly evaluated to meet critical
product requirements for model release, to ensure fairness for different groups or …

Visual analytics systems integrate interactive visualizations and machine learning to enable
expert users to solve complex analysis tasks. Applications combine techniques from various …

In machine learning (ML), ensemble methods-such as bagging, boosting, and stacking-are
widely-established approaches that regularly achieve top-notch predictive performance …

This paper provides a comprehensive survey of anomaly detection for the Internet of Things
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …

The machine learning (ML) life cycle involves a series of iterative steps, from the effective
gathering and preparation of the data—including complex feature engineering processes …

Artificial immune system is derived from the biological immune system. This system is an
important method for generating detectors that include self-adaption, self-regulation and self …

Circular glyphs are used across disparate fields to represent multidimensional data.
However, although these glyphs are extremely effective, creating them is often laborious …

During the training phase of machine learning (ML) models, it is usually necessary to
configure several hyperparameters. This process is computationally intensive and requires …

Industrial product testing is frequently performed in cycles, resulting in cycle-dependent test
data. Monitoring the condition of products under test involves analysis of large and complex …