Investigation of a hybrid algorithm for decision tree generation

We describe experiments with machine learning algorithms (ID3, C4.5, Bagged-C4.5, Boosted-C4.5 and Naive Bayes) and an algorithm made on the basis of a combination of genetic algorithms (GA) and ID3. To perform the experiments, the latter algorithm is implemented as an extension of the MLC++ library of Stanford University. The behaviour of the algorithm is tested using 24 databases including the databases with a large number of attributes. It is shown that owing to "hill-climbing" problem solving, the characteristics of the classifier made with the help of the new algorithm became significantly better. The behaviour of the algorithm is examined when constructing pruned classifiers. The ways to improve standard machine learning algorithms are suggested.