Decision forest for classification of gene expression data.
This study attempts to propose an improved decision forest (IDF) with an integrated graphical user interface. Based on four gene expression data sets, the IDF not only outperforms the original decision forest, but also is superior or comparable to other state-of-the-art machine learning methods, especially in dealing with high dimensional data. With an integrated built-in feature selection (FS) mechanism and fewer parameters to tune, it can be trained more efficiently than methods such as support vector machine, and can be built with much fewer trees than other popular tree-based ensemble methods. Moreover, it suffers less from the curse of dimensionality.
Huang
Jianping
J
Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, 388 YuHangTang Road, Hangzhou 310058, China.
Fang
Hong
H
Fan
Xiaohui
X
D000465:Algorithms; D001185:Artificial Intelligence; D056486:Chemical and Drug Induced Liver Injury / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D030541:Databases, Genetic; D003663:Decision Trees; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D007938:Leukemia / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D014584:User-Computer Interface