Ensemble Learning in Data Mining of Fetal Cardiotocograms

Xiao Yu Chen; Bo Liu; Xin Xia

doi:10.4028/www.scientific.net/AMR.945-949.2505

Paper Titles

Application of ADE-FDTD Method in Lossy Lorentz Media
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The Differential Measurement Model that under the Equal Principle and its Application
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Chaotic Time Series Adaptive Prediction Based on Volterra Series
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Correlation Coefficient Matrix Based PD Feature Dimensional Reduction
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Ensemble Learning in Data Mining of Fetal Cardiotocograms
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Analysis of LED Light Source Energy Consumption for Lighting System Optimized by BP Neural Network Based on Genetic Algorithm
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Combination Forecasting Method Based on IOWA Operator and Application
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Research and Implement of an Improved Frame-Slotted ALOHA Anti-Collision Algorithm
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An Advanced AOI-Cast Algorithm Based on PCA
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 945-949Ensemble Learning in Data Mining of Fetal...

Ensemble Learning in Data Mining of Fetal Cardiotocograms

Abstract:

ReliefF feature selection and LogitBoost ensemble learning method are employed in the data mining procedure of 2126 fetal cardiotocograms (CTGs). Based on 10 critical features selected by ReliefF and the full 21 features, LogitBoost algorithm almost outperforms the other three ensemble learning methods of Stacking, Bagging and AdaBoostM1 in ACC (%) and AUC in classification, and the ACC (%) and AUC of LogitBoost algorithm are achieved to 94.45% and 0.977 based on the critical features from ReliefF.