Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests

@article{Le2017DifferentialPE,
  title={Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests},
  author={Trang T T Le and W. Kyle Simmons and Masaya Misaki and Jerzy Bodurka and Bill C. White and Jonathan Savitz and Brett A. McKinney},
  journal={Bioinformatics},
  year={2017},
  volume={33 18},
  pages={
          2906-2913
        }
}
Motivation Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been… CONTINUE READING