A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering
In the engineering field, excessive data dimensions affect the efficiency of machine learning and analysis of the relationships between data or features. To render feature dimensionality reduction more effective and faster, this paper proposes a new feature dimensionality reduction approach combinin...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/2879640 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849469519563587584 |
|---|---|
| author | Zhun Cheng Zhixiong Lu |
| author_facet | Zhun Cheng Zhixiong Lu |
| author_sort | Zhun Cheng |
| collection | DOAJ |
| description | In the engineering field, excessive data dimensions affect the efficiency of machine learning and analysis of the relationships between data or features. To render feature dimensionality reduction more effective and faster, this paper proposes a new feature dimensionality reduction approach combining a sampling survey method with a heuristic intelligent optimization algorithm. Drawing on feature selection, this method builds a feature-scoring system and a reduced-dimension length-scoring system based on the sampling survey method. According to feature scores and reduced-dimension lengths, the method selects a number of features and reduced-dimension lengths that are ranked in the front with high scores. This feature dimensionality reduction method allows for in-depth optimal selection of features and reduced-dimension lengths with high scores using an improved heuristic intelligent optimization algorithm. To verify the effectiveness of the dimensionality reduction method, this paper applies it to road roughness time-domain estimation based on vehicle dynamic response and gene-selection research in bioengineering. Results in the first case show that the proposed method can improve the accuracy of road roughness time-domain estimation to above 0.99 and reduce measured data of the vehicle dynamic response, reducing the experimental workload significantly. Results in the second case show that the method can select a set of genes quickly and effectively with high disease recognition accuracy. |
| format | Article |
| id | doaj-art-fd3f26d39f6d43e6ada7f0a99d0d302f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-fd3f26d39f6d43e6ada7f0a99d0d302f2025-08-20T03:25:26ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/28796402879640A Novel Efficient Feature Dimensionality Reduction Method and Its Application in EngineeringZhun Cheng0Zhixiong Lu1College of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, ChinaIn the engineering field, excessive data dimensions affect the efficiency of machine learning and analysis of the relationships between data or features. To render feature dimensionality reduction more effective and faster, this paper proposes a new feature dimensionality reduction approach combining a sampling survey method with a heuristic intelligent optimization algorithm. Drawing on feature selection, this method builds a feature-scoring system and a reduced-dimension length-scoring system based on the sampling survey method. According to feature scores and reduced-dimension lengths, the method selects a number of features and reduced-dimension lengths that are ranked in the front with high scores. This feature dimensionality reduction method allows for in-depth optimal selection of features and reduced-dimension lengths with high scores using an improved heuristic intelligent optimization algorithm. To verify the effectiveness of the dimensionality reduction method, this paper applies it to road roughness time-domain estimation based on vehicle dynamic response and gene-selection research in bioengineering. Results in the first case show that the proposed method can improve the accuracy of road roughness time-domain estimation to above 0.99 and reduce measured data of the vehicle dynamic response, reducing the experimental workload significantly. Results in the second case show that the method can select a set of genes quickly and effectively with high disease recognition accuracy.http://dx.doi.org/10.1155/2018/2879640 |
| spellingShingle | Zhun Cheng Zhixiong Lu A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering Complexity |
| title | A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering |
| title_full | A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering |
| title_fullStr | A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering |
| title_full_unstemmed | A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering |
| title_short | A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering |
| title_sort | novel efficient feature dimensionality reduction method and its application in engineering |
| url | http://dx.doi.org/10.1155/2018/2879640 |
| work_keys_str_mv | AT zhuncheng anovelefficientfeaturedimensionalityreductionmethodanditsapplicationinengineering AT zhixionglu anovelefficientfeaturedimensionalityreductionmethodanditsapplicationinengineering AT zhuncheng novelefficientfeaturedimensionalityreductionmethodanditsapplicationinengineering AT zhixionglu novelefficientfeaturedimensionalityreductionmethodanditsapplicationinengineering |