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...

We propose a generic decision tree framework that supports reusable components design. The proposed generic decision tree framework consists of several sub-problems which were recognized by analyzing well-known decision tree induction algorithms, namely ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE, and CTREE. We identified reusable components in these algorithms as well as in several of their p...

This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems ...

Intrusion Detection is an efficient way of detecting the abnormal behavior of packets in the network, Although in data mining there are various effective decision tree based algorithms are implemented for the classification and detection of Intrusions in KDDCup99 Dataset. Here an efficient technique is implemented for the classification and detection of Intrusions in KDDCup99 Dataset using Feat...

__________________________________________________________________________________________ Abstract: Data mining is method of maintaining a large amount of data stored in the database. Decision tree is a technique of data mining which classify the data and produces valuable results. These results are used in analysis and future prediction. The prime objective of this research work is to present...