首页|MFRAG: Multi-Fitness RankAggreg Genetic Algorithm for biomarker selection from microarray data
MFRAG: Multi-Fitness RankAggreg Genetic Algorithm for biomarker selection from microarray data
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
Microarray technology allows the simultaneous study of up to thousands of gene expressions, which is essential for researchers to understand cancer, diagnose disease, and drugs development. However, the large number of irrelevant and redundant genes in the data is a challenge to the performance and efficiency of the feature selection method and the classification algorithms. furthermore, the characteristic of the small data sample size increases the risk of over-fitting. Therefore, an efficient, accurate, and robust feature selection pipeline is necessary for gene selection. This paper proposed a new hybrid feature selection method Multi-Fitness RankAggreg Genetic Algorithm(MFRAG). Considering that a single model tends to overfit on small sample datasets, MFRAG integrates nine feature selection methods that can evaluate feature weights to evaluate individuals and calculates individual fitness by ensemble models. MFRAG more clearly imitates the natural principle of "survival of the fittest." It improves the selection and mutation processes in genetic algorithms, enhances the stability and reliability of the selection process through fusion mechanisms and integrated models, and guides the evolutionary process through a set of lists generated by a feature fusion model. The experimental section compares MFRAG with six standard feature selection methods on ten publicly available microarray data and 18 published state-of-the-art methods on three datasets that researchers widely use. The results show that MFRAG outperforms all standard methods in classification accuracy and number of features and is ahead of most advanced methods.
NSTL
Elsevier
