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

View Article: PubMed Central - PubMed

ABSTRACT

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.

Conclusion: 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.

No MeSH data available.


Fitting of the ECDF using the GL algorithm An example showing the approximation of an ECDF using a generalized logistic (GL) function
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Fig1: Fitting of the ECDF using the GL algorithm An example showing the approximation of an ECDF using a generalized logistic (GL) function

Mentions: and the most suitable value for Q0 is the root of Eq. (11). The root can be resolved numerically and quickly by using the Newton’s method. With this initialization, we could find a set of parameters which make the GL function fit the ECDF well, as shown in Fig. 1.Fig. 1


A robust data scaling algorithm to improve classification accuracies in biomedical data
Fitting of the ECDF using the GL algorithm An example showing the approximation of an ECDF using a generalized logistic (GL) function
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5016890&req=5

Fig1: Fitting of the ECDF using the GL algorithm An example showing the approximation of an ECDF using a generalized logistic (GL) function
Mentions: and the most suitable value for Q0 is the root of Eq. (11). The root can be resolved numerically and quickly by using the Newton’s method. With this initialization, we could find a set of parameters which make the GL function fit the ECDF well, as shown in Fig. 1.Fig. 1

View Article: PubMed Central - PubMed

ABSTRACT

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.

Conclusion: 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.

No MeSH data available.