High‐dimensional spectral data classification with nonparametric feature screening. (26th December 2019)

• Record Type: Journal Article
• Authors: Li, Chuan‐Quan; Xu, Qing‐Song
• Is Part Of: Journal of chemometrics. Volume 34:Number 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-26
• Subjects: classification -- high‐dimensional spectral datasets -- nonparametric feature screening -- random forest; Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.3199
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract: Two nonparametric feature screening methods, namely, the Kolmogorov filter and model free, marginally measure the relationship between categorical response and predictor variables without the parametrical assumption. And they can select important variables in the high‐dimensional classification data. Random forest, as a classical nonparametric method, can solve various classification problems. In this paper, we combine the two nonparametric feature screening methods with random forest to handle with spectral data classification. And then other conventional classification methods are compared with ours on three spectral datasets. The comparison results illustrated that our methods have more desirable ability about classification performance and variable selection than other methods. Abstract : ▪▪▪