Consolidated trees: Classifiers with stable explanation. A model to achieve the desired stability in explanation

Abstract

In real world problems solved with machine learning techniques, achieving small error rates is important, but there are situations where an explanation is compulsory. In these situations the stability of the given explanation is crucial. We have presented a methodology for building classification trees, Consolidated Trees Construction Algorithm (CTC). CTC is based on subsampling techniques, therefore it is suitable to face class imbalance problems, and it improves the error rate of standard classification trees and has larger structural stability. The built trees are more steady as the number of subsamples used for induction increases, and therefore also the explanation related to the classification is more steady and wider. In this paper a model is presented for estimating the number of subsamples that would be needed to achieve the desired structural convergence level. The values estimated using the model and the real values are very similar, and there are not statistically significant differences. © Springer-Verlag Berlin Heidelberg 2005.