A Nearest Hyperrectangle Learning Method
Machine Learning
Trading MIPS and memory for knowledge engineering
Communications of the ACM
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
Automated Support for Building and Extending Expert Models
Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Machine Learning
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hi-index | 12.05 |
Relatively few publications compare machine learning models with expert systems when applied to the same problem domain. Most publications emphasize those cases where the former beat the latter. Is it a realistic picture of the state of the art? Some other findings are presented here. The accuracy of a real world ''mind crafted'' credit scoring expert system is compared with dozens of machine learning models. The results show that while some machine learning models can surpass the expert system's accuracy with statistical significance, most models do not. More interestingly, this happened only when the problem was treated as regression. In contrast, no machine learning model showed any statistically significant advantage over the expert system's accuracy when the same problem was treated as classification. Since the true nature of the class data was ordinal, the latter is the more appropriate setting. It is also shown that the answer to the question is highly dependent on the meter that is being used to define accuracy.