A key aspect of the design of evolutionary and swarm intelligence algorithms is studying
their performance. Statistical comparisons are also a crucial part which allows for reliable …

The machine learning community adopted the use of null hypothesis significance testing
(NHST) in order to ensure the statistical validity of results. Many scientific fields however …

Develo** accurate analytical credit scoring models has become a major focus for financial
institutions. For this purpose, numerous classification algorithms have been proposed for …

Predictive monitoring of business processes is concerned with the prediction of ongoing
cases on a business process. Lately, the popularity of deep learning techniques has …

Algorithm selection as well as hyperparameter optimization are tedious task that have to be
dealt with when applying machine learning to real-world problems. Sequential model-based …

Credit scoring is an effective tool for banks or lending institutions to identify potential bad
lenders and creditworthy applicants. Boosting ensemble approaches have made appealing …

The measurement of progress using benchmarks evaluations is ubiquitous in computer
science and machine learning. However, common approaches to analyzing and presenting …

Abstract Spaces with locally varying scale of measurement, like multidimensional structures
with differently scaled dimensions, are pretty common in statistics and machine learning …

Data is always a crucial issue of concern especially during its prediction and computation in
digital revolution. This paper exactly helps in providing efficient learning mechanism for …

Although being a crucial question for the development of machine learning algorithms, there
is still no consensus on how to compare classifiers over multiple data sets with respect to …