Applying Cross-Permutation-Based Quad-Hybrid Feature Selection Algorithm on Transient Univariates to Select Optimal Features for Transient Analysis

Mosavi, Seyed Alireza Bashiri

doi:10.1109/access.2022.3166917

Cited by 4 publications

(7 citation statements)

References 35 publications

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“…In comparison to the alternative methods, TMFS 45 , GSCLM 46 , and Bi QFS 23 , the suggested feature selection method consistently exhibits a surprising and higher accuracy level over the spectrum of NTS values.…”

Section: Results Analysismentioning

confidence: 86%

“…The precision levels of various approaches differ across the spectrum of NTS values and samples. Comparing the suggested technique to the other methods—TMFS 45 , GSCLM 46 , and Bi QFS 23 —clearly exhibits a remarkably high level of precision sets.…”

Section: Results Analysismentioning

confidence: 87%

“…The suggested feature selection technique consistently displays a significant and superior recall in contrast to the alternative methods, including TMFS 45 , GSCLM 46 , and Bi QFS 23 , across the whole range of NTS values and samples. The suggested strategy maintains a continuously higher recall even while the NTS values change.…”

Section: Results Analysismentioning

confidence: 92%

“…Derived from the Virus share 57 , the VirusShare Dataset is a repository of malware samples to provide security researchers, incident responders, forensic analysts, and the morbidly curious access to samples of live malicious code. Performance of the proposed algorithm is compared to that of three state-of-the-art algorithms for these samples: Bi-objective Quantum-inspired Feature Selection (Bi QFS) 23 , Greedy Search Combined with Local Metaheuristics (GSCLM) 46 , and Traditional Metaheuristic Feature Selection (TMFS) 45 .…”

Section: Results Analysismentioning

confidence: 99%

“…PSO simulates the social behavior of birds and fish to explore the feature space. Although it has shown promise, PSO tends to get stuck in local minima, leading to suboptimal solutions 23 , 24 . Which is done via use of Quad-Hybrid Feature Selection Algorithm (QFS) Process.…”

Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

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Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Potharlanka,

2024

Sci Rep

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Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges.

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Section: Results Analysismentioning

confidence: 86%

Section: Results Analysismentioning

confidence: 87%

Section: Results Analysismentioning

confidence: 92%

Section: Results Analysismentioning

confidence: 99%

Section: In-depth Review Of Existing Machine Learning Models Used For...mentioning

confidence: 99%

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Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Potharlanka,

2024

Sci Rep

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Feature Selection of Multi-class Data Sets Based on Enhanced Binary Gray Wolf Algorithm and Ant Colony Optimization Algorithm

Selvi,

Bibi

2024

SN COMPUT. SCI.

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Surviving Most Relevant Features on Transient Trajectories Data by Dyadic 24-Way Hybrid Feature Selection Algorithm for Transient Stability Prediction

Mosavi

2022

IEEE Access

Self Cite

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Designing an effective feature selection scheme (FSS) is an inevitable solution for top-level balancing contrastive-correlated indices, namely transient processing time (TPT) and transient prediction accuracy (TPA) on transient stability assessment (TSA). Achieving low TPT and high TPA have a tight relationship in selecting the most relevant transient point features (MRTPFs) survived by applying comprehensive FSS on m-variate transient trajectory features (mVTTFs). Hence, we introduce dyadic 24way hybrid FSS (D24WHFSS) to select MRTPFs from mVTTFs. The D24WHFSS comprises 24 permutations of the chained four-stage hybrid structure called 24-way hybrid FSS (24WHFSS). The 24WHFSS raised by bi-incremental wrapper mechanism (bi-IWM) contains incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr). Each hybrid scenario is equipped with symmetric uncertainty (SU) (filter phase) and dual support vector-based classifiers (DSVCs) (wrapper phase). Embedded DSVCs into IWSS/ IWSSr include kernel support vector machine (kSVM) and k-twin SVM (kTWSVM). By plugging dual kernel function pairs (DKFPs) into DSVCs, 24-way SU bi-IWM DSVCs is exerted in the varied twofold repetition (dyadic 24WHFSS). In the first KFP (KFP 1 ), the radial basis function (RBF) is situated in the DSVCs of bi-IWM. In KFP 2 , the dynamic time warping (DTW) and

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Applying Cross-Permutation-Based Quad-Hybrid Feature Selection Algorithm on Transient Univariates to Select Optimal Features for Transient Analysis

Cited by 4 publications

References 35 publications

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Feature Selection of Multi-class Data Sets Based on Enhanced Binary Gray Wolf Algorithm and Ant Colony Optimization Algorithm

Surviving Most Relevant Features on Transient Trajectories Data by Dyadic 24-Way Hybrid Feature Selection Algorithm for Transient Stability Prediction

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