Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE
Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficienc...
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| Published in | Genes Vol. 9; no. 6; p. 301 |
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| Format | Journal Article |
| Language | English |
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| Abstract | Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE. |
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| AbstractList | Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE. Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE.Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE. |
| Author | Chen, Qi Su, Ran Meng, Zhaopeng Liu, Xinyi Jin, Qianguo |
| AuthorAffiliation | 1 School of Computer Software, Tianjin University, Tianjin 300350, China; joannaxiaoqi@163.com (Q.C.); mengzp@tju.edu.cn (Z.M.); xinyiliu@tju.edu.cn (X.L.); qgking@tju.edu.cn (Q.J.) 3 Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China 4 State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300074, China 2 The Military Transportation Command Department, Army Military Transportation University, Tianjin 300361, China |
| AuthorAffiliation_xml | – name: 3 Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China – name: 4 State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin 300074, China – name: 1 School of Computer Software, Tianjin University, Tianjin 300350, China; joannaxiaoqi@163.com (Q.C.); mengzp@tju.edu.cn (Z.M.); xinyiliu@tju.edu.cn (X.L.); qgking@tju.edu.cn (Q.J.) – name: 2 The Military Transportation Command Department, Army Military Transportation University, Tianjin 300361, China |
| Author_xml | – sequence: 1 givenname: Qi surname: Chen fullname: Chen, Qi – sequence: 2 givenname: Zhaopeng surname: Meng fullname: Meng, Zhaopeng – sequence: 3 givenname: Xinyi surname: Liu fullname: Liu, Xinyi – sequence: 4 givenname: Qianguo surname: Jin fullname: Jin, Qianguo – sequence: 5 givenname: Ran orcidid: 0000-0001-5922-0364 surname: Su fullname: Su, Ran |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29914084$$D View this record in MEDLINE/PubMed |
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| Title | Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE |
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