Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance
Feature screening is an important and challenging topic in current class-imbalance learning. Most of the existing feature screening algorithms in class-imbalance learning are based on filtering techniques. However, the variable rankings obtained by various filtering techniques are generally differen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Metabolites |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-1989/11/6/389 |
| _version_ | 1797530184393424896 |
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| author | Guang-Hui Fu Jia-Bao Wang Min-Jie Zong Lun-Zhao Yi |
| author_facet | Guang-Hui Fu Jia-Bao Wang Min-Jie Zong Lun-Zhao Yi |
| author_sort | Guang-Hui Fu |
| collection | DOAJ |
| description | Feature screening is an important and challenging topic in current class-imbalance learning. Most of the existing feature screening algorithms in class-imbalance learning are based on filtering techniques. However, the variable rankings obtained by various filtering techniques are generally different, and this inconsistency among different variable ranking methods is usually ignored in practice. To address this problem, we propose a simple strategy called rank aggregation with re-balance (RAR) for finding key variables from class-imbalanced data. RAR fuses each rank to generate a synthetic rank that takes every ranking into account. The class-imbalanced data are modified via different re-sampling procedures, and RAR is performed in this balanced situation. Five class-imbalanced real datasets and their re-balanced ones are employed to test the RAR’s performance, and RAR is compared with several popular feature screening methods. The result shows that RAR is highly competitive and almost better than single filtering screening in terms of several assessing metrics. Performing re-balanced pretreatment is hugely effective in rank aggregation when the data are class-imbalanced. |
| first_indexed | 2024-03-10T10:25:23Z |
| format | Article |
| id | doaj.art-6344418377f843a9afa1c53ebf56213c |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-10T10:25:23Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-6344418377f843a9afa1c53ebf56213c2023-11-22T00:04:15ZengMDPI AGMetabolites2218-19892021-06-0111638910.3390/metabo11060389Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-BalanceGuang-Hui Fu0Jia-Bao Wang1Min-Jie Zong2Lun-Zhao Yi3School of Science, Kunming University of Science and Technology, Kunming 650500, ChinaSchool of Science, Kunming University of Science and Technology, Kunming 650500, ChinaSchool of Science, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Agriculture and Food, Kunming University of Science and Technology, Kunming 650500, ChinaFeature screening is an important and challenging topic in current class-imbalance learning. Most of the existing feature screening algorithms in class-imbalance learning are based on filtering techniques. However, the variable rankings obtained by various filtering techniques are generally different, and this inconsistency among different variable ranking methods is usually ignored in practice. To address this problem, we propose a simple strategy called rank aggregation with re-balance (RAR) for finding key variables from class-imbalanced data. RAR fuses each rank to generate a synthetic rank that takes every ranking into account. The class-imbalanced data are modified via different re-sampling procedures, and RAR is performed in this balanced situation. Five class-imbalanced real datasets and their re-balanced ones are employed to test the RAR’s performance, and RAR is compared with several popular feature screening methods. The result shows that RAR is highly competitive and almost better than single filtering screening in terms of several assessing metrics. Performing re-balanced pretreatment is hugely effective in rank aggregation when the data are class-imbalanced.https://www.mdpi.com/2218-1989/11/6/389class-imbalancefeature screeningrank aggregationre-balancefiltering algorithm |
| spellingShingle | Guang-Hui Fu Jia-Bao Wang Min-Jie Zong Lun-Zhao Yi Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance Metabolites class-imbalance feature screening rank aggregation re-balance filtering algorithm |
| title | Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance |
| title_full | Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance |
| title_fullStr | Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance |
| title_full_unstemmed | Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance |
| title_short | Feature Ranking and Screening for Class-Imbalanced Metabolomics Data Based on Rank Aggregation Coupled with Re-Balance |
| title_sort | feature ranking and screening for class imbalanced metabolomics data based on rank aggregation coupled with re balance |
| topic | class-imbalance feature screening rank aggregation re-balance filtering algorithm |
| url | https://www.mdpi.com/2218-1989/11/6/389 |
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