Recursive Feature Elimination Based Feature Selection in Modulation Classification for MIMO Systems

Zhou, Shuai; Li, Tao; Li, Yongzhao

doi:10.23919/cje.2021.00.347

Cited by 9 publications

(8 citation statements)

References 26 publications

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“…Liu J [ 31 ] proposed the random forest algorithm with weights, introduced weighting techniques in the construction of decision trees, and used voting with weights in the decision process to improve the prediction ability. The feature selection method represents the objects in the original dataset with a subset of features and removes redundant feature information [ 32 ]. Parlak B [ 33 ] extracted more representative and discriminative features, which effectively improved the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

Li,

Fox

2023

PLoS ONE

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The sudden resignation of core employees often brings losses to companies in various aspects. Traditional employee turnover theory cannot analyze the unbalanced data of employees comprehensively, which leads the company to make wrong decisions. In the face the classification of unbalanced data, the traditional Support Vector Machine (SVM) suffers from insufficient decision plane offset and unbalanced support vector distribution, for which the Synthetic Minority Oversampling Technique (SMOTE) is introduced to improve the balance of generated data. Further, the Fuzzy C-mean (FCM) clustering is improved and combined with the SMOTE (IFCM-SMOTE-SVM) to new synthesized samples with higher accuracy, solving the drawback that the separation data synthesized by SMOTE is too random and easy to generate noisy data. The kernel function is combined with IFCM-SMOTE-SVM and transformed to a high-dimensional space for clustering sampling and classification, and the kernel space-based classification algorithm (KS-IFCM-SMOTE-SVM) is proposed, which improves the effectiveness of the generated data on SVM classification results. Finally, the generalization ability of KS-IFCM-SMOTE-SVM for different types of enterprise data is experimentally demonstrated, and it is verified that the proposed algorithm has stable and accurate performance. This study introduces the SMOTE and FCM clustering, and improves the SVM by combining the data transformation in the kernel space to achieve accurate classification of unbalanced data of employees, which helps enterprises to predict whether employees have the tendency to leave in advance.

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Section: Introductionmentioning

confidence: 99%

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

Li,

Fox

2023

PLoS ONE

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“…Although they are computationally expensive, wrapper methods like Recursive Feature Elimination often outperform filter methods. The downside is that they are prone to overfitting and are not suitable for high-dimensional datasets due to computational intensity levels 8 – 10 .…”

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

confidence: 99%

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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“…Recursive elimination(RFE) is a feature selection algorithm [6] ,It used a base model for multiple rounds of training, after training of each round, delete the weak features until get enough features demand, the base model we chose was SVR. The feature subsets were filtered out through recursive elimination method are shown in Table 1.…”

Section: Feature Selection Based On Recursive Eliminationmentioning

confidence: 99%

“…The results of the XGBoost prediction model for octane number (RON) loss were shown in Figure 1. From Figure 1, it can be seen that the alternative parameters of the XGBoost prediction model for octane number (RON) loss were: number of base trainers: [100,200,300,400,500,600,700]; The maximum depth of the decision tree [1,2,3,4,5,6,7];…”

Section: Establishment and Evaluation Of Modelsmentioning

confidence: 99%

Predictive modeling of gasoline octane loss based on RFE, XGBoost and LightGBM algorithm features

Cong

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Octane value (RON) is a factor which can represent the combustion performance， a decreased RON loss forecast model based on RFE, XGBoost and LightGBM was proposed in this paper. Firstly we used these 3 effective method to filter the feature subset out, each subset included 30 variables, then picked up the most optimized subset which contained the 30 variables. Using of the XGBoost regression method to establish the RON loss forecast model. This model performed well and stable.

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Recursive Feature Elimination Based Feature Selection in Modulation Classification for MIMO Systems

Cited by 9 publications

References 26 publications

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

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

Predictive modeling of gasoline octane loss based on RFE, XGBoost and LightGBM algorithm features

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