J. Gama. Local cascade generalization. In Proc. 15th International Conf. on Machine Learning, pages 206--214. Morgan Kaufmann, San Francisco, CA, 1998. Home/Search Document Details and Download
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Building Optimal Committees of Genetic Programs - Zhang, Joung (2000) (1 citation) (Correct)
....was less than 7 on average. Table 1 compares the average generalization error for the decision tree learner C5.0, C5.0Boosting, standard genetic programming (GP) and the GP commiittee using the presented committee selection method. Here the baseline results by C5.0 are the values reported in [20]. As in [20] we evaluated the results by using ten standard 10 fold crossvalidations. The standard GP achieved better performances for C5.0 in the two problems (breast cancer and heart disease) out of four. The GP committee improved the performance of the standard GP and achieved better results ....
....than 7 on average. Table 1 compares the average generalization error for the decision tree learner C5.0, C5.0Boosting, standard genetic programming (GP) and the GP commiittee using the presented committee selection method. Here the baseline results by C5.0 are the values reported in [20] As in [20], we evaluated the results by using ten standard 10 fold crossvalidations. The standard GP achieved better performances for C5.0 in the two problems (breast cancer and heart disease) out of four. The GP committee improved the performance of the standard GP and achieved better results than C5.0 in ....
Gama, J.: Local Cascade Generalization. In Proceedings of the Fifth International Conference (ICML'98), (1998) 206-214.
Hybrid Decision Tree Learners with Alternative Leaf.. - Seewald, Petrak, Widmer (2000) (Correct)
.... in the leaves; however, they predict numeric values rather than discrete classes (and thus they also use a di erent attribute selection criterion during tree construction) Our hybrid learners are also related to general meta learning approaches like stacking [23] or cascade generalization [6], where di erent learning algorithms are combined by learning a classi cation model from the predictions of a set of base learners. There, the goal is to improve predictive accuracy by combining the opinions of several classi ers; the objective in our hybrid learners, on the other hand, is to ....
Gama J.: Local Cascade Generalization, in Shavlik J.(ed.), Proceedings of the 15th International Conference on Machine Learning (ICML '98), Morgan Kaufmann, Los Altos/Palo Alto/San Francisco, pp.206-214, 1998.
Learning to Form Dynamic Committees - Santiago Onta On (2003) (Correct)
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J. Gama. Local cascade generalization. In Proc. 15th International Conf. on Machine Learning, pages 206--214. Morgan Kaufmann, San Francisco, CA, 1998.
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