scholarly journals Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine

Sensors ◽  
2014 ◽  
Vol 14 (7) ◽  
pp. 12784-12802 ◽  
Author(s):  
Xiaoou Li ◽  
Xun Chen ◽  
Yuning Yan ◽  
Wenshi Wei ◽  
Z. Wang
Keyword(s):  
Support Vector Machine ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Support Vector ◽  
Learning Support ◽  
Eeg Signals ◽  
Multiple Kernel

scholarly journals A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers

2020 ◽  
Vol 36 (12) ◽  
pp. 3766-3772 ◽  
Author(s):  
Arezou Rahimi ◽  
Mehmet Gönen
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Late Stage ◽  
Multiple Kernel Learning ◽  
Genomic Data ◽  
Kernel Learning ◽  
Disease Stage ◽  
Support Vector ◽  
Genomic Information ◽  
Multiple Kernel

Abstract Motivation Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression of the disease stage is of great interest. Given that there are similarities in the underlying mechanisms of different cancers, in addition to the considerable correlation in the genomic data, there is a need for machine learning methods that can take these aspects of genomic data into account. Furthermore, using machine learning for studying multiple cancer cohorts together with a collection of molecular pathways creates an opportunity for knowledge extraction. Results We studied the problem of discriminating early- and late-stage tumors of several cancers using genomic information while enforcing interpretability on the solutions. To this end, we developed a multitask multiple kernel learning (MTMKL) method with a co-clustering step based on a cutting-plane algorithm to identify the relationships between the input tasks and kernels. We tested our algorithm on 15 cancer cohorts and observed that, in most cases, MTMKL outperforms other algorithms (including random forests, support vector machine and single-task multiple kernel learning) in terms of predictive power. Using the aggregate results from multiple replications, we also derived similarity matrices between cancer cohorts, which are, in many cases, in agreement with available relationships reported in the relevant literature. Availability and implementation Our implementations of support vector machine and multiple kernel learning algorithms in R are available at https://github.com/arezourahimi/mtgsbc together with the scripts that replicate the reported experiments. Supplementary information Supplementary data are available at Bioinformatics online.

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