A robust data scaling algorithm to improve classification accuracies in biomedical data.
BACKGROUND
Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. While mainstream biomedical informatics research focuses on developing more accurate models, the importance of data preprocessing draws less attention. We propose the Generalized Logistic (GL) algorithm that scales data uniformly to an appropriate interval by learning a generalized logistic function to fit the empirical cumulative distribution function of the data. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is particularly suitable for diagnostic/classification models in clinical/medical applications where the number of samples is usually small; it scales the data in a nonlinear fashion, which leads to potential improvement in accuracy.


RESULTS
To evaluate the effectiveness of the proposed algorithm, we conducted experiments on 16 binary classification tasks with different variable types and cover a wide range of applications. The resultant performance in terms of area under the receiver operation characteristic curve (AUROC) and percentage of correct classification showed that models learned using data scaled by the GL algorithm outperform the ones using data scaled by the Min-max and the Z-score algorithm, which are the most commonly used data scaling algorithms.


CONCLUSIONS
The proposed GL algorithm is simple and effective. It is robust to outliers, so no additional denoising or outlier detection step is needed in data preprocessing. Empirical results also show models learned from data scaled by the GL algorithm have higher accuracy compared to the commonly used data scaling algorithms.
Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, 1925 North 12th Street, Philadelphia, 19122, USA.
Xi Hang
XH
Cao
Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, 1925 North 12th Street, Philadelphia, 19122, USA.|Signals and Systems Department, School of Electrical Engineering, Bulevar kralja Aleksandra 73, Belgrade, 11120, Serbia.
Ivan
I
Stojkovic
Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, 1925 North 12th Street, Philadelphia, 19122, USA. zoran.obradovic@temple.edu.
Zoran
Z
Obradovic
D000465:Algorithms; D035843:Biomedical Research; D019992:Databases as Topic; D006801:Humans; D008962:Models, Theoretical; D012372:ROC Curve
Classification model; Data normalization; Data scaling; Empirical cumulative distribution function; Generalized logistic function; Outlier