Two-stage classification with SIS using a new filter ranking method in high throughput data

Sangjin Kim, Jong Min Kim

    Research output: Contribution to journalArticlepeer-review

    4 Scopus citations

    Abstract

    Over the last decade, high dimensional data have been popularly paid attention to in bioinformatics. These data increase the likelihood of detecting the most promising novel information. However, there are limitations of high-performance computing and overfitting issues. To overcome the issues, alternative strategies need to be explored for the detection of true important features. A two-stage approach, filtering and variable selection steps, has been receiving attention. Filtering methods are divided into two categories of individual ranking and feature subset selection methods. Both have issues with the lack of consideration for joint correlation among features and computing time of an NP-hard problem. Therefore, we proposed a new filter ranking method (PF) using the elastic net penalty with sure independence screening (SIS) based on resampling technique to overcome these issues. We demonstrated that SIS-LASSO, SIS-MCP, and SIS-SCAD with the proposed filtering method achieved superior performance of not only accuracy, AUROC, and geometric mean but also true positive detection compared to those with the marginal maximum likelihood ranking method (MMLR) through extensive simulation studies. In addition, we applied it in a real application of colon and lung cancer gene expression data to investigate the classification performance and power of detecting true genes associated with colon and lung cancer.

    Original languageEnglish (US)
    Article number493
    JournalMathematics
    Volume7
    Issue number6
    DOIs
    StatePublished - Jun 1 2019

    Bibliographical note

    Publisher Copyright:
    © 2019 by the authors.

    Keywords

    • AUROC
    • Accuracy
    • Elastic net
    • Geometric mean
    • LASSO
    • MCP
    • SCAD
    • SIS

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