Font Size:
Random Garden: a Supervised Learning Algorithm
Last modified: 2018-05-18
Abstract
Classification and Regression Trees model and two ways for ensembling them are considered, aimed to predict a binary response. The first one is Random Forest and the second is Random Garden, presented in this work. The feature selection impact on the different algorithms is investigated. The described procedures are applied to 18 Customer Relationship Management data sets constructed in Banking field.
References
1. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
2. Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer series
in statistics, Springer, Berlin (2001)
3. John, G., Kohavi, R., Pfleger, K.: Irrelevant Features and the Subset Selection Problem. Pro-
ceedings of the Eleventh International Conference on Machine Learning 129, 121–129 (1994)
4. Nguyen, T.T., Huang, J.Z., Nguyen, T.T.: Unbiased Feature Selection in Learning Ran-
dom Forests for High-Dimensional Data. The Scientific World Journal (2015) doi:
10.1155/2015/471371
2. Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer series
in statistics, Springer, Berlin (2001)
3. John, G., Kohavi, R., Pfleger, K.: Irrelevant Features and the Subset Selection Problem. Pro-
ceedings of the Eleventh International Conference on Machine Learning 129, 121–129 (1994)
4. Nguyen, T.T., Huang, J.Z., Nguyen, T.T.: Unbiased Feature Selection in Learning Ran-
dom Forests for High-Dimensional Data. The Scientific World Journal (2015) doi:
10.1155/2015/471371
Full Text:
PDF