The two dominant schemes for rule-learning, C4. 5 and RIPPER, both operate in two stages.
First they induce an initial rule set and then they refine it using a rather complex optimization …

Motivated by a computer experiment for the design of a rocket booster, this article explores
nonstationary modeling methodologies that couple stationary Gaussian processes with …

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network,
the Classification and Regression Tree, and the Random Forest model is proposed, and its …

Feature selection plays an important role in classification for several reasons. First it can
simplify the model and this way computational cost can be reduced and also when the …

Histograms are convenient non-parametric density estimators, which continue to be used
ubiquitously. Summary quantities estimated from histogram-based probability density …

OBJECTIVE: Diabetes affects between 2% and 4% of the global population (up to 10% in
the over 65 age group), and its avoidance and effective treatment are undoubtedly crucial …

Traditional clustering algorithms, used in data mining for transactional databases, arc mainly
concerned with grou** transactions, but they do not generally provide an adequate …

The k-nearest neighbor (KNN) rule is a classical and yet very effective nonparametric
technique in pattern classification, but its classification performance severely relies on the …

Classification is a frequently encountered data mining problem. Decision tree techniques
have been widely used to build classification models as such models closely resemble …

The benefits obtained from the decomposition of a classification task involving several
classes, into a set of smaller classification problems involving two classes only, usually …