Surrogate-Assisted and Filter-Based Multiobjective Evolutionary Feature Selection for Deep Learning

Espinosa, Raquel; Jiménez, Fernando; Palma, José

doi:10.1109/tnnls.2023.3234629

Cited by 9 publications

(3 citation statements)

References 53 publications

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“…Thus, these two MOEAs have been adopted in the experiment as algorithms to compare our proposed one against. Nevertheless, there are still many other MOEAs that have been reported in recent years that can be used for tackling multi-objective feature selection, such as the variable granularity searchbased algorithm in [64], the surrogate-assisted and filter-based algorithm in [65], and the steering-matrix-based algorithm in [66].…”

Section: Evolutionary Feature Selectionmentioning

confidence: 99%

A Hybrid Initialization and Effective Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective Large-Scale Feature Selection in Classification

Xu,

Huang,

Wen

et al. 2024

Mathematics

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Evolutionary algorithms have been widely used for tackling multi-objective optimization problems, while feature selection in classification can also be seen as a discrete bi-objective optimization problem that pursues minimizing both the classification error and the number of selected features. However, traditional multi-objective evolutionary algorithms (MOEAs) can encounter setbacks when the dimensionality of features explodes to a large scale, i.e., the curse of dimensionality. Thus, in this paper, we focus on designing an adaptive MOEA framework for solving bi-objective feature selection, especially on large-scale datasets, by adopting hybrid initialization and effective reproduction (called HIER). The former attempts to improve the starting state of evolution by composing a hybrid initial population, while the latter tries to generate more effective offspring by modifying the whole reproduction process. Moreover, the statistical experiment results suggest that HIER generally performs the best on most of the 20 test datasets, compared with six state-of-the-art MOEAs, in terms of multiple metrics covering both optimization and classification performances. Then, the component contribution of HIER is also studied, suggesting that each of its essential components has a positive effect. Finally, the computational time complexity of HIER is also analyzed, suggesting that HIER is not time-consuming at all and shows promising computational efficiency.

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Section: Evolutionary Feature Selectionmentioning

confidence: 99%

A Hybrid Initialization and Effective Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective Large-Scale Feature Selection in Classification

Xu,

Huang,

Wen

et al. 2024

Mathematics

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show abstract

“…Evolutionary algorithms (EAs) have been widely applied to multi-objective feature selection [18,31,32], but most studies merely apply EAs without considering the relationships among features. We recognize that, with the increasing complexity of real-world data, especially the rise of big data, features in high-dimensional datasets are likely to encompass various complex relationships [33] which might not be merely linearly positive or negative correlations.…”

Section: Major Motivationsmentioning

confidence: 99%

A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection

Zhou,

Zheng,

et al. 2024

Applied Sciences

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In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. This paper introduces a Knowledge-Guided Competitive Co-Evolutionary Algorithm (KCCEA) for feature selection, especially for high-dimensional features. In the proposed algorithm, we make improvements to the foundational dominance-based multi-objective evolutionary algorithm in two aspects. First, the use of feature correlation as knowledge to guide evolution enhances the search speed and quality of traditional multi-objective evolutionary algorithm solutions. Second, a dynamically allocated competitive–cooperative evolutionary mechanism is proposed, integrating the improved knowledge-guided evolution with traditional evolutionary algorithms, further enhancing the search efficiency and diversity of solutions. Through rigorous empirical testing on various datasets, the KCCEA demonstrates superior performance compared to basic multi-objective evolutionary algorithms, providing effective solutions to multi-objective feature selection problems while enhancing the interpretability and effectiveness of prediction models.

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“…Hernandez, Muratet, Pierotti, and Carron [23] provided an introduction to the principles, motivations, and significant deep learning approaches. Additionally, in reference [24], Espinosa, Jimenez, and Palma conducted a review of the advancements made in feature learning and deep learning from the representation learning perspective. Zhang, Sjarif, and Ibrahim [25] presented an exposition on the advancement of deep learning, as well as significant models within this field, such as convolutional neural networks and recurrent neural networks.…”

Section: Advanced Deep Learningmentioning

confidence: 99%

Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models

Endo

2023

JRS

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Representation learning or feature learning refers to a collection of methods employed in machine learning, which allows systems to autonomously determine representations needed for classifications or feature detection from unprocessed data. Representation learning algorithms are specifically crafted to acquire knowledge of conceptual features that define data. The field of state representation learning is centered on a specific type of representation learning that involves the acquisition of low-dimensional learned features that undergo temporal evolution and are subject to the influence of an agent's actions. Over the past few years, deep architecture have been widely employed for representation learning and have demonstrated exceptional performance in various tasks, including but not limited to object detection, speech recognition, and image classification. This article provides a comprehensive overview of the evolution of techniques for data representation learning. Our research focuses on the examination of conventional feature learning algorithms and advanced deep learning models. This paper presents an introduction to data representation learning history, along with a comprehensive list of available resources such as online courses, tutorials, and books. Additionally, various tool-boxes are also provided for further exploration in this field. In conclusion, this article presents remarks and future prospects for data representation learning.

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Surrogate-Assisted and Filter-Based Multiobjective Evolutionary Feature Selection for Deep Learning

Cited by 9 publications

References 53 publications

A Hybrid Initialization and Effective Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective Large-Scale Feature Selection in Classification

A Hybrid Initialization and Effective Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective Large-Scale Feature Selection in Classification

A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection

Analysis of Conventional Feature Learning Algorithms and Advanced Deep Learning Models

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