A constructive evolutionary approach for feature selection in unsupervised learning

In this paper, a novel Constructive Genetic Algorithm (CGA) for the feature selection in clustering problem is addressed. This issue has become a challenge since the data sets dimension increased exponentially over the years. In order to evaluate the CGA performance, the Genetic Algorithm (GA) has also been executed to be compared to the first one. The modeling and execution of this evolutionary approach to this problem are unpublished in the literature. For the results emission, twelve data sets have been used, of which four were simulated and eight are real data sets. The results showed that both approaches overperformed the no feature selection data sets. However, the CGA presented a better performance than GA in eight of the twelve data sets regarding solution quality. Considering the execution time, the CGA obtained exceptional results, that is, it spent less time than the GA in most data sets.