Feature Selection with Mutual Information for Regression Problems
Selecting relevant features for machine learning modeling improves the performance of the learning methods. Mutual information (MI) is known to be used as relevant criterion for selecting feature subsets from input dataset with a nonlinear relationship to the predicting attribute. However, mutu...
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2015
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my.unimas.ir.134472017-02-14T05:47:20Z http://ir.unimas.my/id/eprint/13447/ Feature Selection with Mutual Information for Regression Problems Muhammad Aliyu, Sulaiman Jane, Labadin T Technology (General) Selecting relevant features for machine learning modeling improves the performance of the learning methods. Mutual information (MI) is known to be used as relevant criterion for selecting feature subsets from input dataset with a nonlinear relationship to the predicting attribute. However, mutual information estimator suffers the following limitation; it depends on smoothing parameters, the feature selection greedy methods lack theoretically justified stopping criteria and in theory it can be used for both classification and regression problems, however in practice more often it formulation is limited to classification problems. This paper investigates a proposed improvement on the three limitations of the Mutual Information estimator (as mentioned above), through the use of resampling techniques and formulation of mutual information based on differential entropic for regression problems. 2015 Conference or Workshop Item PeerReviewed text en http://ir.unimas.my/id/eprint/13447/1/Feature%20Selection%20with%20Mutual%20Information%20%28abstract%29.pdf Muhammad Aliyu, Sulaiman and Jane, Labadin (2015) Feature Selection with Mutual Information for Regression Problems. In: 2015 9th International Conference on IT in Asia (CITA) : Transforming Big Data into Knowledge, 4-5 August 2015, Kuching, Sarawak Malaysia. |
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Malaysia |
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Universiti Malaysia Sarawak |
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| language |
English |
| topic |
T Technology (General) |
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T Technology (General) Muhammad Aliyu, Sulaiman Jane, Labadin Feature Selection with Mutual Information for Regression Problems |
| description |
Selecting relevant features for machine learning
modeling improves the performance of the learning methods.
Mutual information (MI) is known to be used as relevant
criterion for selecting feature subsets from input dataset with a
nonlinear relationship to the predicting attribute. However,
mutual information estimator suffers the following limitation; it
depends on smoothing parameters, the feature selection greedy
methods lack theoretically justified stopping criteria and in
theory it can be used for both classification and regression
problems, however in practice more often it formulation is
limited to classification problems. This paper investigates a
proposed improvement on the three limitations of the Mutual
Information estimator (as mentioned above), through the use of
resampling techniques and formulation of mutual information
based on differential entropic for regression problems. |
| format |
Conference or Workshop Item |
| author |
Muhammad Aliyu, Sulaiman Jane, Labadin |
| author_facet |
Muhammad Aliyu, Sulaiman Jane, Labadin |
| author_sort |
Muhammad Aliyu, Sulaiman |
| title |
Feature Selection with Mutual Information for Regression Problems |
| title_short |
Feature Selection with Mutual Information for Regression Problems |
| title_full |
Feature Selection with Mutual Information for Regression Problems |
| title_fullStr |
Feature Selection with Mutual Information for Regression Problems |
| title_full_unstemmed |
Feature Selection with Mutual Information for Regression Problems |
| title_sort |
feature selection with mutual information for regression problems |
| publishDate |
2015 |
| url |
http://ir.unimas.my/id/eprint/13447/1/Feature%20Selection%20with%20Mutual%20Information%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/13447/ |
| _version_ |
1644511662944813056 |
| score |
13.251813 |