Abstract
BackgroundMachine 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.ResultsTo 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.ConclusionThe 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.
Highlights
Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support
In this manuscript we have significantly extended our preliminary work in the following ways: 1. providing a thorough description of the proposed Generalized Logistic (GL) algorithm as well as intuitive and qualitative explanations of scenarios where the new algorithm is superior to the Min-max and Z-score algorithms; 2. extending the GL algorithm to include a much better and more general parameter initialization for the non-convex optimization, which is a critical part of the algorithm for fitting the generalized logistic function to the empirical cumulative distribution function; 3. empirically demonstrating that the GL algorithm is effective in gene expression classification tasks, and in a broad variety of different diagnostic/classification tasks with different types of variables
Comparing the GL algorithm to the Z-score algorithm and the Min-max algorithm, in most tasks, models learned with the data scaled by the GL algorithm achieved the best average area under the receiver operation characteristic curve (AUROC)’s and the best average accuracies
Summary
Machine learning models have been adapted in biomedical research and practice for knowledge discovery and decision support. The GL algorithm is simple yet effective; it is intrinsically robust to outliers, so it is 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. A model should extract the existing signal from the data and disregard any spurious patterns (noise). This is not an easy task, since data are often far from perfect; some of the imperfections include irrelevant variables, small numbers of samples, missing values, and outliers
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