array(2) { ["lab"]=> string(4) "1409" ["publication"]=> string(5) "12612" } Robust Sparse Logistic Regression With the Lq(0<q<1) Regularization for Feature Selection Using Gene Expression Data - Liang Yong | LabXing

Liang Yong

简介 中藥質量控制資訊系統，生物資訊學

大学 Macau University of Science and Technology / Faculty of Information Technology
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Robust Sparse Logistic Regression With the Lq(0<q<1) Regularization for Feature Selection Using Gene Expression Data

2018
期刊 IEEE Access
Microarray technology is a popular technique that has been extensively applied in cancer diagnosis. Many studies have used high-dimensional microarray data to identify informative features to classify the types of cancer, yet numerous irrelevant features that exist in microarray data may introduce the noise and decrease classification accuracy. Regularization techniques are common methods for feature selection, which can be used to reduce irrelevant features and avoid overfitting. In recent years, different regularization methods have been proposed. Theoretically, the L q (0

  • 卷 6
  • 页码 68586-68595
  • IEEE
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