An evolutionary algorithm for constructing a decision forest: Combining the classification of disjoints decision trees

Decision forest is an ensemble classification method that combines multiple decision trees to in a manner that results in more accurate classifications. By combining multiple heterogeneous decision trees, decision forest is effective in mitigating noise that is often prevalent in real-world classification tasks. This paper presents a new genetic algorithm for constructing a decision forest. Each decision tree classifier is trained using a disjoint set of attributes. Moreover, we examine the effectiveness of using a Vapnik-Chervonenkis dimension bound for evaluating the fitness function of decision forest. The new algorithm was tested on various datasets. The obtained results have been compared to other methods, indicating the superiority of the proposed algorithm.