Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

With the advent of automated machine learning, automated hyperparameter optimization
methods are by now routinely used in data mining. However, this progress is not yet …

In meta-learning, classification problems can be described by a variety of features, including
complexity measures. These measures allow capturing the complexity of the frontier that …

Mental workload assessment of the operators in some safety-critical human-machine
systems is an important research topic. In this paper, an experiment was designed to obtain …

Cyber security in the context of big data is known to be a critical problem and presents a
great challenge to the research community. Machine learning algorithms have been …

The machine learning algorithms exploit a given dataset in order to build an efficient
predictive or descriptive model. Multi-objective evolutionary optimization assists machine …

In the view of difficulties in bruise detection of potatoes, a texture recognition technique
based on hyperspectral imaging and discrete wavelet transform was presented and …

To efficiently solve complex optimization problems, numerous population-based meta-
heuristics and extensions have been developed. However, the performances of the …

Label noise can be a major problem in classification tasks, since most machine learning
algorithms rely on data labels in their inductive process. Thereupon, various techniques for …

Sentiment analysis and classification task is used in recommender systems to analyze
movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums …