The classification problem can be addressed by numerous techniques and algorithms which
belong to different paradigms of machine learning. In this paper, we are interested in …

Data Mining (DM) is the process for automatic discovery of high level knowledge by
obtaining information from real world, large and complex data sets [26], and is the core step …

In this paper, traditional and meta-heuristic approaches for optimizing deep neural networks
(DNN) have been surveyed, and a genetic algorithm (GA)-based approach involving two …

Subgroup discovery (SD) is a descriptive data mining technique using supervised learning.
In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will …

Microarray techniques are leading to the development of sophisticated algorithms capable
of extracting novel and useful knowledge from a biomedical point of view. In this work, we …

A Chinese handwriting database named HIT-MW is presented to facilitate the offline
Chinese handwritten text recognition. Both the writers and the texts for handcopying are …

Microarray data analysis has been shown to provide an effective tool for studying cancer
and genetic diseases. Although classical machine learning techniques have successfully …

Predictive monitoring of business processes is a challenging topic of process mining which
is concerned with the prediction of process indicators of running process instances. The …

Supervised learning problems can be faced by using a wide variety of approaches
supported in machine learning. In recent years there has been an increasing interest in …

Variation in software requirements, technological upgrade and occurrence of defects
necessitate change in software for its effective use. Early detection of those classes of a …