Muhammad Aliyu, Sulaiman and Jane, Labadin (2015) Feature Selection based on Mutual Information. In: 2015 9th International Conference on IT in Asia (CITA) : Transforming Big Data into Knowledge, 4-5 August 2015, Kuching, Sarawak Malaysia.
Feature Selection based on Mutual Information (abstract).pdf
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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.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||Machine Learning; Mutual Information; Feature Selection, research, Universiti Malaysia Sarawak, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education|
|Subjects:||T Technology > T Technology (General)|
|Divisions:||Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology|
|Depositing User:||Karen Kornalius|
|Date Deposited:||08 Sep 2016 19:27|
|Last Modified:||14 Feb 2017 05:46|
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