MOB-ESP and other Improvements in Probability Estimation
This work addresses the need for better probability estimates in intelligent systems, but it appears incremental as it builds on existing probability estimation trees.
The paper tackled the problem of improving class probability estimation for optimal reasoning under uncertainty in intelligent systems, resulting in MOB-ESP, which outputs significantly more accurate class probabilities and better probability rankings than baseline and enhanced methods, as shown in experiments on benchmark datasets.
A key prerequisite to optimal reasoning under uncertainty in intelligent systems is to start with good class probability estimates. This paper improves on the current best probability estimation trees (Bagged-PETs) and also presents a new ensemble-based algorithm (MOB-ESP). Comparisons are made using several benchmark datasets and multiple metrics. These experiments show that MOB-ESP outputs significantly more accurate class probabilities than either the baseline BPETs algorithm or the enhanced version presented here (EB-PETs). These results are based on metrics closely associated with the average accuracy of the predictions. MOB-ESP also provides much better probability rankings than B-PETs. The paper further suggests how these estimation techniques can be applied in concert with a broader category of classifiers.