C4.5: programs for machine learning
C4.5: programs for machine learning
Technical Note: Bias and the Quantification of Stability
Machine Learning - Special issue on bias evaluation and selection
Knowledge Acquisition form Examples Vis Multiple Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Decision Tree Instability and Active Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
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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.