Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

Li, Mi; Lu, Xiaofeng; Wang, Xiaodong; Lu, Shengfu; Zhong, Ning

doi:10.1080/24699322.2016.1240300

Cited by 11 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SVM model in this study was introduced to discriminate isolates of MRSA from MSSA and validated by using the “e1071” and “caret” packages in R 31,32 . The m/z features screened by Lasso regression were randomly split into subsets with 90% as training data and 10% as test data.…”

Section: Methodsmentioning

confidence: 99%

Rapid identification and discrimination of methicillin‐resistant Staphylococcus aureus strains via matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry

Liu

Hsu

et al. 2020

Rapid Comm Mass Spectrometry

View full text Add to dashboard Cite

Rationale Methicillin‐resistant Staphylococcus aureus (MRSA) is one of major clinical pathogens responsible for both hospital‐ and community‐acquired infections worldwide. A delay in targeted antibiotic treatment contributes to longer hospitalization stay, higher costs, and increasing in‐hospital mortality. Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) has been integrated into the routine workflow for microbial identification over the past decade, and it has also shown promising functions in the detection of bacterial resistance. Therefore, we describe a rapid MALDI‐TOF MS‐based methodology for MRSA screening with machine‐learning algorithms. Methods A total of 452 clinical S. aureus isolates were included in this study, of which 194 were MRSA and 258 were methicillin‐sensitive S. aureus (MSSA). The mass‐to‐charge ratio (m/z) features from MRSA and MSSA strains were binned and selected through Lasso regression. These features were then used to train a non‐linear support vector machine (SVM) with radial basis function (RBF) kernels to evaluate the discrimination performance. The classifiers' accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were evaluated and compared with those from the random forest (RF) model. Results A total of 2601 unique spectral peaks of all isolates were identified and 38 m/z features were selected for the classifying model. The AUCs of the non‐linear RBF‐SVM model and the RF model were 0.89 and 0.87, respectively, and the accuracy ranged between 0.86 (RBF‐SVM) and 0.82 (RF). Conclusions Our study demonstrates that MALDI‐TOF MS coupled with machine‐learning algorithms could be used to develop a rapid and easy‐to‐use method to discriminate MRSA from MSSA. Considering that this method is easy to implement in routine microbiology laboratories, it suggests a cost‐effective and time‐efficient alternative to conventional resistance detection in the future to improve clinical treatment.

show abstract

Section: Methodsmentioning

confidence: 99%

Rapid identification and discrimination of methicillin‐resistant Staphylococcus aureus strains via matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry

Liu

Hsu

et al. 2020

Rapid Comm Mass Spectrometry

View full text Add to dashboard Cite

show abstract

“…These operations directly affected the kernel matrix and the operation result was the positive semi-definite matrix at all times. The polynomial kernel function was a global kernel function that provided a better dissemination capability and a weaker learning ability [33], while the sigmoid kernel function provided a better global performance [46].…”

Section: Svm Based On Hybrid Kernelsmentioning

confidence: 99%

Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study

Hoshyar

Rashidi

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms.

show abstract

“…Further understanding of the nature of kernel functions will help to build a more powerful SVM classifier. In 2001, Scholkopf et al first divided the kernel function into local kernel function and global kernel function [32]. Surveys conducted by Simts and Jordaan [33] showed that local kernels have a good interpolation ability, while global kernels have a good extrapolation ability.…”

Section: Related Workmentioning

confidence: 99%

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

2019

View full text Add to dashboard Cite

As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.

show abstract

Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

Cited by 11 publications

References 32 publications

Rapid identification and discrimination of methicillin‐resistant Staphylococcus aureus strains via matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry

Rapid identification and discrimination of methicillin‐resistant Staphylococcus aureus strains via matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry

Proposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

Contact Info

Product

Resources

About