SVM kernel based on particle swarm optimized vector and Bayesian optimized SVM in atmospheric particulate matter forecasting

Kouziokas, Georgios N.

doi:10.1016/j.asoc.2020.106410

Cited by 70 publications

(25 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, a model considering various variables was proposed by combining ensemble learning and multi-layer perceptron (MLP) [ 16 , 17 , 18 , 19 , 20 ]. Many diverse studies have been conducted, in which particle swarms have been optimized using swarm intelligence in combination with ANNs [ 21 , 22 , 23 , 24 , 25 ]. In the field of residual modeling, multiple studies have been conducted on hybrid models, in order to improve the performance of predicting PM concentration levels [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

Park

Chang

2021

IJERPH

View full text Add to dashboard Cite

Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM2.5 (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively.

show abstract

Section: Introductionmentioning

confidence: 99%

A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

Park

Chang

2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…The choice of penalty parameter c and g in SVM kernel function is directly related to the effectiveness and accuracy of SVM algorithm in solving dichotomy. According to previous research methods, there are mainly 5 optimization methods for the above two important parameters, namely, empirical selection method, grid selection method, genetic optimization algorithm, particle swarm optimization algorithm, and ant colony optimization algorithm so on ( Ali and Abdullah, 2020 ; Kouziokas, 2020 ; Li X. et al, 2020 ; Arya Azar et al, 2021 ; Ramkumar et al, 2021 ). Although these optimization algorithms have been applied to some extent and achieved some effects, they all have problems of different degrees.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Algorithm for Computer-Aided Diagnosis of Breast Cancer Based on Support Vector Machine

Dou

Meng

2021

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

As one of the most vulnerable cancers of women, the incidence rate of breast cancer in China is increasing at an annual rate of 3%, and the incidence is younger. Therefore, it is necessary to conduct research on the risk of breast cancer, including the cause of disease and the prediction of breast cancer risk based on historical data. Data based statistical learning is an important branch of modern computational intelligence technology. Using machine learning method to predict and judge unknown data provides a new idea for breast cancer diagnosis. In this paper, an improved optimization algorithm (GSP_SVM) is proposed by combining genetic algorithm, particle swarm optimization and simulated annealing with support vector machine algorithm. The results show that the classification accuracy, MCC, AUC and other indicators have reached a very high level. By comparing with other optimization algorithms, it can be seen that this method can provide effective support for decision-making of breast cancer auxiliary diagnosis, thus significantly improving the diagnosis efficiency of medical institutions. Finally, this paper also preliminarily explores the effect of applying this algorithm in detecting and classifying breast cancer in different periods, and discusses the application of this algorithm to multiple classifications by comparing it with other algorithms.

show abstract

“…At present, it is under the background of large samples in the era of big data. Due to its super large amount of calculation in large samples, the attention of SVM has declined, but it is still a commonly used machine learning algorithm [9,18,26]. The applications of the SVM have been significantly increased in the last years in multiple sectors as a successful machine learning approach in modeling the relationship between the input and the output in regression problems [8,30,31].…”

Section: Introductionmentioning

confidence: 99%

Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning

Liu

2021

ITC

View full text Add to dashboard Cite

Least squares support vector machine (LSSVM) is a machine learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, and it has good performance in the data processingof small samples. The LSSVM model lacks sparsity and is unable to handle large-scale data problem, this articleproposes an LSSVM method based on mixture kernel learning and sparse samples. This algorithm reduces theinitial training set to a sub-dataset using a sparse selection strategy. It converts the single kernel function in theLSSVM model into a mixed kernel function and optimizes its parameters. The reduced sub-dataset is used fortraining LSSVM. Finally, a group of datasets in the UCI Machine Learning Repository were used to verify theeffectiveness of the proposed algorithm, which is applied to real-world power load data to achieve better fittingand improve the prediction accuracy.

show abstract

SVM kernel based on particle swarm optimized vector and Bayesian optimized SVM in atmospheric particulate matter forecasting

Cited by 70 publications

References 37 publications

A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

An Optimization Algorithm for Computer-Aided Diagnosis of Breast Cancer Based on Support Vector Machine

Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning

Contact Info

Product

Resources

About