Classification with imbalanced data-sets has become one of the most challenging problems
in Data Mining. Being one class much more represented than the other produces …

Semi-supervised learning is one of the most promising learning paradigms in many practical
applications where few labeled samples are available. Among such learning models, semi …

This paper presents the implementation of a new text document classification framework that
uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean …

Feature selection is an important task in many machine learning and data mining problems.
Due to the increasing size of datasets, the removal of redundant, erroneous or noisy features …

A general approach to classifier combination considers each model as a probabilistic
classifier which outputs a class membership posterior probability. In this general scenario, it …

We developed a new machine learning-based method in order to facilitate the
manufacturing processes of pharmaceutical products, such as tablets, in accordance with …

During the drug development process, it is common to carry out toxicity tests and adverse
effect studies, which are essential to guarantee patient safety and the success of the …

Bagging and Boosting are two main ensemble approaches consolidating the decisions of
several hypotheses. The diversity of the ensemble members is considered to be a significant …

We present a method for constructing ensembles of classifiers using supervised projections
of random subspaces. The method combines the philosophy of boosting, focusing on difficult …

Abstract Traditional Support Vector Machine (SVM) solution suffers from O (n 2) time
complexity, which makes it impractical to very large datasets. To reduce its high …