Feature Selection based on Mutual Information
The application of machine learning models such as support vector machine (SVM) and artificial neural networks (ANN) in predicting reservoir properties has been effective in the recent years when compared with the traditional empirical methods. Despite that the machine learning models suffer a l...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/13446/1/Feature%20Selection%20based%20on%20Mutual%20Information%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/13446/ |
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| Summary: | The application of machine learning models such as
support vector machine (SVM) and artificial neural networks
(ANN) in predicting reservoir properties has been effective in the
recent years when compared with the traditional empirical
methods. Despite that the machine learning models suffer a lot in
the faces of uncertain data which is common characteristics of
well log dataset. The reason for uncertainty in well log dataset
includes a missing scale, data interpretation and measurement
error problems. Feature Selection aimed at selecting feature
subset that is relevant to the predicting property. In this paper a
feature selection based on mutual information criterion is
proposed, the strong point of this method relies on the choice of
threshold based on statistically sound criterion for the typical
greedy feedforward method of feature selection. Experimental
results indicate that the proposed method is capable of improving
the performance of the machine learning models in terms of
prediction accuracy and reduction in training time. |
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