The MLR package provides a generic, object-oriented, and extensible framework for
classification, regression, survival analysis and clustering for the R language. It provides a …

We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization
(MBO), also known as Bayesian optimization, which addresses the problem of expensive …

Classification is one of the most common machine learning tasks. SVMs have been
frequently applied to this task. In general, the values chosen for the hyper-parameters of …

Currently, consumers are more concerned about freshness and quality of food. Poultry egg
storage time is a freshness and quality indicator in industrial and consumer applications …

Evolutionary algorithms (EAs) have been shown to be powerful tools for complex
optimization problems, which are ubiquitous in both communication and big data analytics …

Supplier evaluation is an important issue in supply chain management. Most existing studies
rely on expert experience to evaluate supplier performance. In order to alleviate the …

The aim of this work is to compare different approaches for parallelization in model-based
optimization. As another alternative aside from the existing methods, we propose using a …

The performance of support vector machines in nonlinearly separable classification
problems strongly relies on the kernel function. Toward an automatic machine learning …

The performance of many machine learning algorithms heavily depends on the setting of
their respective hyperparameters. Many different tuning approaches exist, from simple grid …

The definition of pedestrian behavior when crossing the street and facing potential collision
situations is crucial for the design of new Autonomous Emergency Braking systems (AEB) in …