Radiomics에서 Feature를 선택하는 것은 핵심 중의 핵심이다. 열심히 영상을 다듬고 영상에 대한 여러 value를 뽑아 놓아도 feature selection을 잘못하면 그동안의 노력이 물거품이 되기 때문이다. Feature selection에는 여러 가지 방안들이 제시되어 왔는데 가장 많이 사용되는 방법들을 정리해보고자 한다. In omics experiments, one of the ultimate goals is the identification of features(biomarkers) that are different between treatment groups. One of the very common problems in omics data is that the sample size is small but huge number of features which can lead to over-fitting. What can be alternative methods to overcome this problem? The first paradigm - LASSO : based on classification approaches and compares the least absolute shrinkage and selection operator. - Ridge regression - Elastic Net feature selection methods The second paradigm - using a linear models framework : individual features are modeled separately ignoring the correlation structure among features. Omics data analysing 순서 ⇨ original feature subsets ⇨ classification approach...