Application of Feature Selection Based on Multilayer GA in Stock Prediction
This paper proposes a feature selection model based on a multilayer genetic algorithm (GA) to select the features of a high stock dividend (HSD) and eliminate the relatively redundant features in the optimal solution by using layer-by-layer information transfer and two-dimensionality reduction metho...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/14/7/1415 |
| _version_ | 1797415393345667072 |
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| author | Xiaoning Li Qiancheng Yu Chen Tang Zekun Lu Yufan Yang |
| author_facet | Xiaoning Li Qiancheng Yu Chen Tang Zekun Lu Yufan Yang |
| author_sort | Xiaoning Li |
| collection | DOAJ |
| description | This paper proposes a feature selection model based on a multilayer genetic algorithm (GA) to select the features of a high stock dividend (HSD) and eliminate the relatively redundant features in the optimal solution by using layer-by-layer information transfer and two-dimensionality reduction methods. Combining the ensemble model and time-series split cross-validation (TSCV) indicator as the fitness function solves the problem of selecting the fitness function for each layer. The symmetry character of the model is fully utilized in the two-dimensionality reduction processes, according to the change in data dimensions and the unbalanced characteristics of the HSD, setting the corresponding TSCV indicators. We built seven ensemble prediction models for actual stock trading data for comparison experiments. The results show that the feature selection model based on multilayer GA can effectively eliminate the relatively redundant features after dimensionality reduction and significantly improve the balancing accuracy, precision and AUC performance of the seven ensemble learning models. Finally, adversarial validation is used to analyze the differences in the balanced accuracy of the training and test sets caused by the inconsistent distribution of the data sets. |
| first_indexed | 2024-03-09T05:46:58Z |
| format | Article |
| id | doaj.art-3f25095022b74395bf975f60856140e3 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T05:46:58Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-3f25095022b74395bf975f60856140e32023-12-03T12:19:55ZengMDPI AGSymmetry2073-89942022-07-01147141510.3390/sym14071415Application of Feature Selection Based on Multilayer GA in Stock PredictionXiaoning Li0Qiancheng Yu1Chen Tang2Zekun Lu3Yufan Yang4School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, ChinaSchool of Computer Science and Engineering, North Minzu University, Yinchuan 750021, ChinaSchool of Computer Science and Engineering, North Minzu University, Yinchuan 750021, ChinaSchool of Computer Science and Engineering, North Minzu University, Yinchuan 750021, ChinaSchool of Computer Science and Engineering, North Minzu University, Yinchuan 750021, ChinaThis paper proposes a feature selection model based on a multilayer genetic algorithm (GA) to select the features of a high stock dividend (HSD) and eliminate the relatively redundant features in the optimal solution by using layer-by-layer information transfer and two-dimensionality reduction methods. Combining the ensemble model and time-series split cross-validation (TSCV) indicator as the fitness function solves the problem of selecting the fitness function for each layer. The symmetry character of the model is fully utilized in the two-dimensionality reduction processes, according to the change in data dimensions and the unbalanced characteristics of the HSD, setting the corresponding TSCV indicators. We built seven ensemble prediction models for actual stock trading data for comparison experiments. The results show that the feature selection model based on multilayer GA can effectively eliminate the relatively redundant features after dimensionality reduction and significantly improve the balancing accuracy, precision and AUC performance of the seven ensemble learning models. Finally, adversarial validation is used to analyze the differences in the balanced accuracy of the training and test sets caused by the inconsistent distribution of the data sets.https://www.mdpi.com/2073-8994/14/7/1415genetic algorithmtime series split cross validationfitness functionfeature selectionstock prediction |
| spellingShingle | Xiaoning Li Qiancheng Yu Chen Tang Zekun Lu Yufan Yang Application of Feature Selection Based on Multilayer GA in Stock Prediction Symmetry genetic algorithm time series split cross validation fitness function feature selection stock prediction |
| title | Application of Feature Selection Based on Multilayer GA in Stock Prediction |
| title_full | Application of Feature Selection Based on Multilayer GA in Stock Prediction |
| title_fullStr | Application of Feature Selection Based on Multilayer GA in Stock Prediction |
| title_full_unstemmed | Application of Feature Selection Based on Multilayer GA in Stock Prediction |
| title_short | Application of Feature Selection Based on Multilayer GA in Stock Prediction |
| title_sort | application of feature selection based on multilayer ga in stock prediction |
| topic | genetic algorithm time series split cross validation fitness function feature selection stock prediction |
| url | https://www.mdpi.com/2073-8994/14/7/1415 |
| work_keys_str_mv | AT xiaoningli applicationoffeatureselectionbasedonmultilayergainstockprediction AT qianchengyu applicationoffeatureselectionbasedonmultilayergainstockprediction AT chentang applicationoffeatureselectionbasedonmultilayergainstockprediction AT zekunlu applicationoffeatureselectionbasedonmultilayergainstockprediction AT yufanyang applicationoffeatureselectionbasedonmultilayergainstockprediction |