Attribute Selection Measure in Decision Tree Growing

SLEAS: Supervised Learning using Entropy as Attribute Selection Measure

There is embryonic importance in scaling up the broadly used decision tree learning algorithms to huge datasets. Even though abundant diverse methodologies have been proposed, a fast tree growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential to a greater extent. This paper aims at improving the performance of the SLIQ (Supervised Le...

Automatic Selection of Split Criterion during Tree Growing Based on Node Location

Typically, decision tree construction algorithms apply a single \goodness of split" criterion to form each test node of the tree. It is a hypothesis of this research that better results can be obtained if during tree construction one applies a split criterion suited to the \location" of the test node in the tree. Speciically, given the objective of maximizing predictive accuracy, test nodes nea...

Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection

Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...

A framework for bottom-up induction of oblique decision trees

Decision-tree induction algorithms are widely used in knowledge discovery and data mining, specially in scenarios where model comprehensibility is desired. A variation of the traditional univariate approach is the so-called oblique decision tree, which allows multivariate tests in its non-terminal nodes. Oblique decision trees can model decision boundaries that are oblique to the attribute axes...

Decision Tree Induction 17.1 Introduction 17.2 Attribute selection measure 17.3 Tree Pruning 17.4 Extracting Classification Rules from Decision Trees 17.5 Bayesian Classification 17.6 Bayes Theorem 17.7 Naïve Bayesian Classification 17.8 Bayesian Belief Networks