Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Xie, Hailun; Zhang, Li; Lim, Chee Peng; Yu, Yonghong; Liu, Han

doi:10.3390/s21051816

Cited by 27 publications

(16 citation statements)

References 77 publications

(148 reference statements)

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“…The aim was to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The proposed models illustrated statistical superiority for discriminative feature selection for a total of 13 data sets [14].…”

Section: Related Workmentioning

confidence: 98%

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PSO-UFS: A Novel Approach to Univariate Feature Selection Using Particle Swarm Optimization

Bouchene

2024

Preprint

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Univariate Feature Selection (UFS) traditionally involves a labor-intensive process of trial-and-error, necessitating the selection of scoring functions and the determination of feature numbers. These choices can inadvertently affect both the performance and interpretability of the model. To address this challenge, we introduce Particle Swarm Optimization for Univariate Feature Selection (PSO-UFS), an innovative method that automates these crucial decisions. PSO-UFS leverages the power of Particle Swarm Optimization (PSO) to autonomously identify the optimal scoring function and feature subset that maximize a machine learning algorithm's performance metric. Our empirical evaluations across multiple datasets demonstrate that PSO-UFS significantly outperforms traditional UFS in various performance metrics, including accuracy, precision, recall, and F1-score. Importantly, PSO-UFS generates more interpretable feature subsets, thereby enhancing the model's comprehensibility. This advancement paves the way for broader applications in real-world scenarios where feature reduction and interpretability are paramount.

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Section: Related Workmentioning

confidence: 98%

“…Moreover, a study proposed two PSO variants to undertake feature selection tasks [14]. The aim was to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions.…”

Section: Related Workmentioning

confidence: 99%

PSO-UFS: A Novel Approach to Univariate Feature Selection Using Particle Swarm Optimization

Bouchene

2024

Preprint

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“…The outcomes demonstrated the effectiveness of the RF approach for interference signal and PD feature selection, which makes it suitable for power-component PD feature selection. Particle swarm optimization (PSO) and its variations have been frequently adopted as search engines in wrapper-based feature selection approaches because of their rapid convergence speed and high discriminatory ability to search [118]. The PSO algorithm, known for its simplicity, implementation ease, minimal parameters, and high performance, is ideal for complex optimization problems, being utilized in pattern recognition and data classification [119].…”

Section: Random Forestsmentioning

confidence: 99%

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Kumar,

Shafiq,

Kauhaniemi

et al. 2024

Energies

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Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.

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“…Xie et al [3] tried to overcome the poor exploitation and premature convergence of particle swarm optimization (PSO) by presenting two new variants of PSO. In the first variant they integrated global best signals, rectified personal, swarm leader enhancement with Gaussian distribution, local exploitation using spiral search, mutation operations, and mirroring for solution improvement.…”

Section: Related Workmentioning

confidence: 99%

A Discrete Moth Flame Algorithm for Feature Selection

2023

IJIES

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A crucial and important task in machine learning is feature selection (FS). The primary goal of the feature selection task is to minimize the dimension of the feature set while preserving performance accuracy. In order to address the FS task, a discrete moth flame algorithm that is combined with levy flights (DL-MFA) is presented in this research. The proposed DL-MFA imitates the natural navigational patterns of moths. The moths move along a straight line at a constant angle in the direction of the true light source (the moon) known as transverse orientation. Additionally, moths are drawn to artificial lights like fires and because of the close proximity; they constantly adjust their flying angles, creating a spiral path. In order to maintain healthy population diversity and increase the global search capabilities of the algorithm, the levy flight search technique is also used as a regulator of the moth position updating mechanism. The five swarm intelligence algorithms (SIAs) are contrasted with the proposed algorithm using measures such as entropy, purity, completeness score (CS), and homogeneity score (HS). For evaluating fitness, the SSE fitness function is utilised. The outcomes have shown that the proposed algorithm achieved purity values in the range 90% to 100%, and entropy 10% to 50%. Proposed DL-MFA has also achieved homogeneity score and completeness score up to 50%. These results prove that the proposed algorithm is better than its state-ofthe-art competitors.

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Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Cited by 27 publications

References 77 publications

PSO-UFS: A Novel Approach to Univariate Feature Selection Using Particle Swarm Optimization

PSO-UFS: A Novel Approach to Univariate Feature Selection Using Particle Swarm Optimization

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

A Discrete Moth Flame Algorithm for Feature Selection

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