The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …

Microarray data classification is a difficult challenge for machine learning researchers due to
its high number of features and the small sample sizes. Feature selection has been soon …

Semi-supervised classification methods are suitable tools to tackle training sets with large
amounts of unlabeled data and a small quantity of labeled data. This problem has been …

Often real-world datasets are incomplete and contain some missing attribute values.
Furthermore, many data mining and machine learning techniques cannot directly handle …

Data preprocessing is a major and essential stage whose main goal is to obtain final data
sets that can be considered correct and useful for further data mining algorithms. This paper …

This paper introduces the 3rd major release of the KEEL Software. KEEL is an open source
Java framework (GPLv3 license) that provides a number of modules to perform a wide …

Feature selection plays an important role in the machine-vision-based online detection of
foreign fibers in cotton because of improvement detection accuracy and speed. Feature sets …

Feature generation is one of the challenging aspects of machine learning. We present
ExploreKit, a framework for automated feature generation. ExploreKit generates a large set …

Serverless computing is an emerging cloud computing paradigm, being adopted to develop
a wide range of software applications. It allows developers to focus on the application logic …

A large variety of issues influence the success of data mining on a given problem. Two
primary and important issues are the representation and the quality of the dataset …