Deep Learning Model with GA-based Visual Feature Selection and Context Integration

Mandal, Ranju; Azam, Basim; Verma, Brijesh; Zhang, Mengjie

doi:10.1109/cec45853.2021.9504753

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…After 2021, Omidvar, M. et al [17] proposed a neural network feature selection strategy based on GA, using trained neural networks to provide fitness values for each chromosome. Mandal, R. et al [18] introduced a binary class learner based on a binary class learner to learn features optimized by GA. Empirical analysis showed that this method is essential in improving accuracy and producing stable predictions. Zhang, Y. et al [19] proposed a text feature selection method for text classification based on Word2Vec word embedding and a high-level biogenetic selection GA, which effectively reduced the feature dimension and improved the classification effect.…”

Section: Genetic Algorithm For Feature Selectionmentioning

confidence: 99%

Application of Feature Selection Based on Multilayer GA in Stock Prediction

Tang

et al. 2022

Symmetry

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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.

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Section: Genetic Algorithm For Feature Selectionmentioning

confidence: 99%