Parameter identification and prediction of Jiles–Atherton model for DC‐biased transformer using improved shuffled frog leaping algorithm and least square support vector machine

2015 ◽  
Vol 9 (9) ◽  
pp. 660-669 ◽  
Author(s):  
Fenghua Wang ◽  
Chao Geng ◽  
Lei Su
Keyword(s):  
Support Vector Machine ◽  
Parameter Identification ◽  
Least Square ◽  
Support Vector ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping

Early Cancer Detection from Genome-wide Cell-free DNA Fragmentation via Shuffled Frog Leaping Algorithm and Support Vector Machine

2021 ◽  
Author(s):  
Linjing Liu ◽  
Xingjian Chen ◽  
Ka-Chun Wong
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Early Cancer ◽  
Machine Learning Algorithms ◽  
Early Cancer Detection ◽  
Supplementary Information ◽  
Support Vector ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping

Abstract Motivation Early cancer detection is significant for the patient mortality rate reduction. Although machine learning has been widely employed in that context, there are still deficiencies. In this work, we studied different machine learning algorithms for early cancer detection and proposed an Adaptive Support Vector Machine (ASVM) method by synergizing Shuffled Frog Leaping Algorithm (SFLA) and Support Vector Machine (SVM) in this paper. Results As ASVM regulates SVM for parameter adaption based on data characteristics, the experimental results demonstrated the robust generalization capability of ASVM on different datasets under different settings; for instance, ASVM can enhance the sensitivity by over 10% for early cancer detection compared with SVM. Besides, our proposed ASVM outperformed Grid Search + SVM and Random Search + SVM by significant margins in terms of the area under the ROC curve (AUC) (0.938 vs. 0.922 vs. 0.921). Availability The proposed algorithm and dataset are available at https://github.com/ElaineLIU-920/ASVM-for-Early-Cancer-Detection. Supplementary information Supplementary data are available at Bioinformatics online.

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