| 1. |
- Boström, Henrik
(författare)
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Calibrating Random Forests
- 2008
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Ingår i: Proceedings of the Seventh International Conference on Machine Learning and Applications. - : IEEE. - 9780769534954 ; , s. 121-126
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Konferensbidrag (refereegranskat)abstract
- When using the output of classifiers to calculate the expected utility of different alternatives in decision situations, the correctness of predicted class probabilities may be of crucial importance. However, even very accurate classifiers may output class probabilities of rather poor quality. One way of overcoming this problem is by means of calibration, i.e., mapping the original class probabilities to more accurate ones. Previous studies have however indicated that random forests are difficult to calibrate by standard calibration methods. In this work, a novel calibration method is introduced, which is based on a recent finding that probabilities predicted by forests of classification trees have a lower squared error compared to those predicted by forests of probability estimation trees (PETs). The novel calibration method is compared to the two standard methods, Platt scaling and isotonic regression, on 34 datasets from the UCI repository. The experiment shows that random forests of PETs calibrated by the novel method significantly outperform uncalibrated random forests of both PETs and classification trees, as well as random forests calibrated with the two standard methods, with respect to the squared error of predicted class probabilities.
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| 2. |
- Boström, Henrik
(författare)
-
Calibrating Random Forests
- 2008
-
Ingår i: Proceedings of the Seventh International Conference on Machine Learning and Applications (ICMLA'08). - : IEEE Computer Society. - 9780769534954 ; , s. 121-126
-
Konferensbidrag (refereegranskat)abstract
- When using the output of classifiers to calculate the expected utility of different alternatives in decision situations, the correctness of predicted class probabilities may be of crucial importance. However, even very accurate classifiers may output class probabilities of rather poor quality. One way of overcoming this problem is by means of calibration, i.e., mapping the original class probabilities to more accurate ones. Previous studies have however indicated that random forests are difficult to calibrate by standard calibration methods. In this work, a novel calibration method is introduced, which is based on a recent finding that probabilities predicted by forests of classification trees have a lower squared error compared to those predicted by forests of probability estimation trees (PETs). The novel calibration method is compared to the two standard methods, Platt scaling and isotonic regression, on 34 datasets from the UCI repository. The experiment shows that random forests of PETs calibrated by the novel method significantly outperform uncalibrated random forests of both PETs and classification trees, as well as random forests calibrated with the two standard methods, with respect to the squared error of predicted class probabilities.
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| 3. |
- Löfström, Tuve, et al.
(författare)
-
On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers
- 2008
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Ingår i: 2008 Seventh International Conference on Machine Learning and Applications. - : IEEE. - 9780769534954 ; , s. 127-132
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Konferensbidrag (refereegranskat)abstract
- The test set accuracy for ensembles of classifiers selected based on single measures of accuracy and diversity as well as combinations of such measures is investigated. It is found that by combining measures, a higher test set accuracy may be obtained than by using any single accuracy or diversity measure. It is further investigated whether a multi-criteria search for an ensemble that maximizes both accuracy and diversity leads to more accurate ensembles than by optimizing a single criterion. The results indicate that it might be more beneficial to search for ensembles that are both accurate and diverse. Furthermore, the results show that diversity measures could compete with accuracy measures as selection criterion.
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| 4. |
- Sönströd, Cecilia, et al.
(författare)
-
Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes
- 2008
-
Ingår i: Proceedings of the Seventh International Conference on Machine Learning and Applications. - : IEEE. - 9780769534954 ; , s. 559-564
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Konferensbidrag (refereegranskat)abstract
- When using machine learning for in silico modeling, the goal is normally to obtain highly accurate predictive models. Often, however, models should also bring insights into interesting relationships in the domain. It is then desirable that machine learning techniques have the ability to obtain small and transparent models, where the user can control the tradeoff between accuracy, comprehensibility and coverage. In this study, three different decision list algorithms are evaluated on a data set concerning the interaction of molecules with a human gene that regulates heart functioning (hERG). The results show that decision list algorithms can obtain predictive performance not far from the state-of-the-art method random forests, but also that algorithms focusing on accuracy alone may produce complex decision lists that are very hard to interpret. The experiments also show that by sacrificing accuracy only to a limited degree, comprehensibility (measured as both model size and classification complexity) can be improved remarkably.
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