An Ensemble Feature Selection Method for Biomarker Discovery
| Autor: | Hichem Frigui, Xiang Zhang, Biyun Shi, Aliasghar Shahrjooihaghighi, Ameni Trabelsi, Xiaoli Wei |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Computer science business.industry 0206 medical engineering Feature extraction Feature selection 02 engineering and technology Filter (signal processing) Machine learning computer.software_genre Ensemble learning Article Data set 03 medical and health sciences 030104 developmental biology ComputingMethodologies_PATTERNRECOGNITION Discriminative model Robustness (computer science) Artificial intelligence Biomarker discovery business computer 020602 bioinformatics |
| Zdroj: | ISSPIT |
| Popis: | Feature selection in Liquid Chromatography-Mass Spectrometry (LC-MS)-based metabolomics data (biomarker discovery) have become an important topic for machine learning researchers. High dimensionality and small sample size of LC-MS data make feature selection a challenging task. The goal of biomarker discovery is to select the few most discriminative features among a large number of irreverent ones. To improve the reliability of the discovered biomarkers, we use an ensemble-based approach. Ensemble learning can improve the accuracy of feature selection by combining multiple algorithms that have complementary information. In this paper, we propose an ensemble approach to combine the results of filter-based feature selection methods. To evaluate the proposed approach, we compared it to two commonly used methods, t-test and PLS-DA, using a real data set. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|