The construction of a support vector machine using the shuffled frog-leaping algorithm

2015 ◽  
Author(s):  
M. H. Horng ◽  
C. F. Chao ◽  
H. Z. Chai
Keyword(s):  
Support Vector Machine ◽  
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.

scholarly journals Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Lijun Wang ◽  
Shengfei Ji ◽  
Nanyang Ji
Keyword(s):  
Support Vector Machine ◽  
Global Optimization ◽  
Fault Diagnosis ◽  
Model Simulation ◽  
Rolling Bearing ◽  
Support Vector ◽  
Denoising Method ◽  
Svm Algorithm ◽  
Svm Model ◽  
Shuffled Frog Leaping

This paper presents a method that combines Shuffled Frog Leaping Algorithm (SFLA) with Support Vector Machine (SVM) method in order to identify the fault types of rolling bearing in the gearbox. The proposed method improves the accuracy of fault diagnosis identification after processing the collected vibration signals through wavelet threshold denoising. The global optimization and high computational efficiency of SFLA are applied to the SVM model. Simulation results show that the SFLA-SVM algorithm is effective in fault diagnosis. Compared with SVM and Particle Swarm Optimization SVM (PSO-SVM) algorithms, it is demonstrated that the SFLA-SVM algorithm has the advantages of better global optimization, higher accuracy, and better reliability of diagnosis. Its accuracy is further improved through the integration of the wavelet threshold denoising method.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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