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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A robust data scaling algorithm to improve classification accuracies in biomedical data.

Xi Hang Cao¹, Ivan Stojkovic^1,2, Zoran Obradovic³

¹Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, 1925 North 12th Street, Philadelphia, 19122, USA.

BMC Bioinformatics

|September 11, 2016

Summary

This summary is machine-generated.

A new Generalized Logistic (GL) algorithm offers effective data scaling for machine learning in biomedical informatics. This method improves classification accuracy and is robust to outliers, outperforming standard scaling techniques.

Keywords:

Classification model Data normalization Data scaling Empirical cumulative distribution function Generalized logistic function Outlier

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Area of Science:

Biomedical Informatics
Machine Learning
Data Science

Background:

Machine learning models are increasingly used in biomedical research for knowledge discovery and decision support.
Data preprocessing, particularly data scaling, is crucial but often overlooked in favor of model accuracy.
Existing scaling methods like Min-max and Z-score may not be optimal for all biomedical datasets, especially those with outliers or limited samples.

Purpose of the Study:

To introduce and evaluate the Generalized Logistic (GL) algorithm for data scaling in machine learning.
To demonstrate the effectiveness of the GL algorithm in improving classification accuracy for biomedical applications.
To compare the GL algorithm against commonly used data scaling methods.

Main Methods:

The Generalized Logistic (GL) algorithm was developed to scale data uniformly by fitting a generalized logistic function to the empirical cumulative distribution function.
The GL algorithm is designed to be intrinsically robust to outliers and performs nonlinear data scaling.
Experiments were conducted on 16 binary classification tasks across various applications to assess performance.

Main Results:

Models trained on data scaled using the GL algorithm demonstrated superior performance compared to Min-max and Z-score scaling.
Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and percentage of correct classification.
The GL algorithm showed improved accuracy in classification tasks, particularly beneficial for small sample sizes common in clinical settings.

Conclusions:

The proposed GL algorithm is a simple yet effective data preprocessing technique for machine learning in biomedical informatics.
Its robustness to outliers eliminates the need for separate denoising or outlier detection steps.
Empirical results confirm that the GL algorithm enhances model accuracy compared to traditional scaling methods.