NDLI: Accurate and interpretable regression trees using oracle coaching

Content Provider	IEEE Xplore Digital Library
Author	Johansson, U. Sonstrod, C. Konig, R.
Copyright Year	2014
Description	Author affiliation: Sch. of Bus. & IT, Univ. of Boras, Boras, Sweden (Johansson, U.; Sonstrod, C.; Konig, R.)
Abstract	In many real-world scenarios, predictive models need to be interpretable, thus ruling out many machine learning techniques known to produce very accurate models, e.g., neural networks, support vector machines and all ensemble schemes. Most often, tree models or rule sets are used instead, typically resulting in significantly lower predictive performance. The overall purpose of oracle coaching is to reduce this accuracy vs. comprehensibility trade-off by producing interpretable models optimized for the specific production set at hand. The method requires production set inputs to be present when generating the predictive model, a demand fulfilled in most, but not all, predictive modeling scenarios. In oracle coaching, a highly accurate, but opaque, model is first induced from the training data. This model (“the oracle”) is then used to label both the training instances and the production instances. Finally, interpretable models are trained using different combinations of the resulting data sets. In this paper, the oracle coaching produces regression trees, using neural networks and random forests as oracles. The experiments, using 32 publicly available data sets, show that the oracle coaching leads to significantly improved predictive performance, compared to standard induction. In addition, it is also shown that a highly accurate opaque model can be successfully used as a pre-processing step to reduce the noise typically present in data, even in situations where production inputs are not available. In fact, just augmenting or replacing training data with another copy of the training set, but with the predictions from the opaque model as targets, produced significantly more accurate and/or more compact regression trees.
Starting Page	194
Ending Page	201
File Size	672706
Page Count	8
File Format	PDF
ISBN	9781479945184
DOI	10.1109/CIDM.2014.7008667
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Interpretable models Regression trees Training data Production Vegetation Predictive models Data models Predictive modeling Data mining Oracle coaching
Content Type	Text
Resource Type	Article

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