A Survey of Evolutionary Algorithms for Decision-Tree Induction

Evolutionary Algorithms in Decision Tree Induction

Survey of Distributed Decision Tree Induction Techniques

The decision tree-based classification is a popular approach for pattern recognition and data mining. Most decision tree induction methods assume training data being present at one central location. Given the growth in distributed databases at geographically dispersed locations, the methods for decision tree induction in distributed settings are gaining importance. This paper describes one such...

Reusable components in decision tree induction algorithms

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...

Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms

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 ...

An Evolutionary Algorithm for Oblique Decision Tree Induction

In the paper, a new evolutionary approach to induction of oblique decision trees is described. In each non-terminal node, the specialized evolutionary algorithm is applied to search for a splitting hyperplane. The feature selection is embedded into the algorithm, which allows to eliminate redundant and noisy features at each node. The experimental evaluation of the proposed approach is presente...