A Hybrid Algorithm Based on PSO and GA for Feature Selection

Yang, Xue; Aouari, Asma; Mansour, Romany F.; Su, Shoubao

doi:10.32604/jcs.2021.017018

Cited by 14 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this article, we proposed a hybrid based method for feature selection called PSO-GA. Particle swarm optimization (PSO) is a fltering processes and efcient method for feature subselection [24]. Te local search competence of PSO is strong but that it cannot accomplish sufcient exploration.…”

Section: Features Selectionmentioning

confidence: 99%

Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

Alatawi

Alsubaie

Khan

et al. 2023

Security and Communication Networks

View full text Add to dashboard Cite

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.

show abstract

Section: Features Selectionmentioning

confidence: 99%

Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

Alatawi

Alsubaie

Khan

et al. 2023

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…The experiment results proved the effectiveness of NBPSOSEE-SBS in reducing the significant number of irrelevant features and improving the prediction results in terms of the lower execution time compared with a well-known algorithm with seven other popular wrapper-based features subset selection techniques used in the prediction of risk of ECR for power customers. Xue et al [ 263 ] proposed a modern hybrid selection algorithm comprising the GA and PSO to enhance the search capabilities of this model, and KNN was utilized as the classifier. The method’s performance was used in some simulations using the learning array from UCI as a benchmark dataset.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

show abstract

“…Compared with genetic algorithm (GA) and ant colony algorithm (ACO), the PSO algorithm has the characteristics of simplicity and efficiency, fewer parameters, and faster convergence speed [11]. Therefore, this paper uses PSO algorithm to determine the weight coefficients of the combined model [26]. And the PSO algorithm is used to solve the combination coefficients of the two models to increase the speed of the solution and give full play to the advantages of the combined model.…”

Section: Prophet-lstm Combined Model Constructionmentioning

confidence: 99%

Time Series Forecasting Fusion Network Model Based on Prophet and營mproved LSTM

Liu¹,

Yu²,

Zhao³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Time series forecasting and analysis are widely used in many fields and application scenarios. Time series historical data reflects the change pattern and trend, which can serve the application and decision in each application scenario to a certain extent. In this paper, we select the time series prediction problem in the atmospheric environment scenario to start the application research. In terms of data support, we obtain the data of nearly 3500 vehicles in some cities in China from Runwoda Research Institute, focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou, Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them. This paper proposes a P-gLSTNet model, and uses Autoregressive Integrated Moving Average model (ARIMA), long and short-term memory (LSTM), and Prophet to predict and compare the emissions in the future period. The experiments are validated on four public data sets and one self-collected data set, and the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are selected as the evaluation metrics. The experimental results show that the proposed P-gLSTNet fusion model predicts less error, outperforms the backbone method, and is more suitable for the prediction of time-series data in this scenario.

show abstract

A Hybrid Algorithm Based on PSO and GA for Feature Selection

Cited by 14 publications

References 0 publications

Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

Multiclass feature selection with metaheuristic optimization algorithms: a review

Time Series Forecasting Fusion Network Model Based on Prophet and營mproved LSTM

Contact Info

Product

Resources

About