Abstract Decision Trees (DTs) are predictive models in supervised learning, known not only
for their unquestionable utility in a wide range of applications but also for their interpretability …

In this paper, a Pearson's correlation coefficient based decision tree (PCC-Tree) is
established and its parallel implementation is developed in the framework of Map-Reduce …

Decision trees are popular as stand-alone classifiers or as base learners in ensemble
classifiers. Mostly, this is due to decision trees having the advantage of being easy to …

Phishing is a cyber-attack which targets naive online users tricking into revealing sensitive
information such as username, password, social security number or credit card number etc …

Decision trees in random forests use a single feature in non-leaf nodes to split the data.
Such splitting results in axis-parallel decision boundaries which may fail to exploit the …

Random Forest is an ensemble of decision trees based on the bagging and random
subspace concepts. As suggested by Breiman, the strength of unstable learners and the …

Decision Tree is widely applied in many areas, such as classification and recognition.
Traditional information entropy and Pearson's correlation coefficient are often applied as …

Abstract Recent studies in Machine Learning indicates that the classifiers most likely to be
the bests are the random forests. As an ensemble classifier, random forest combines …

End product quality prediction is one of the key issues in smart manufacturing. Reliable
evaluation and parameter optimization is needed to ensure their high-quality production …

A novel linear multivariate decision tree classifier, Binary Decision Tree based on K-means
Splitting (BDTKS), is presented in this paper. The unsupervised K-means clustering is …