A new algorithm to build consolidated trees: study of the error rate and steadiness

This paper presents a new methodology for building decision trees, Consolidated Trees Construction algorithm, that improves the behavior of C4.5. It reduces the error and the complexity of the induced trees, being the differences in the complexity statistically significant. The advantage of this methodology in respect to other techniques such as bagging, boosting, etc. is that the final classifier is based on a single tree and not in a set of trees, so that, the explaining capacity of the classification is not lost. The experimentation has been done with some databases of the UCI Repository and a real application of customer fidelization from a company of electrical appliances.