The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure

Abstract

This paper focuses on the comparison of dimensionality reduction effect between LightGBM and XGBoost-FA. With respect to XGBoost, LightGBM can be built in the effect of dimensionality reduction via both Gradient-based One-Side Sampling(GOSS) and Exclusive Feature Bundling(EFB) algorithms, while XGBoost coupling with traditional dimensionality reduction tool Factor Analysis (XGBoost-FA) may also have dimensionality reduction effect. To present the empirical comparison, the prediagnosis dataset for the 2018 Kaggle competition Acute Liver Failure has been chosen as the research object. And pairwise comparison has been conducted among XGBoost, LightGBM, XGBoost-FA and LightGBM-FA. Concerning the test set, the vector (accuracy, log loss function, training time) of the above first four prediagnostic models are (0.75014, 0.569707, 10.5s), (0.75811, 0.576059,15.1s), (0.67786,0.663924,5.7s) and (0.67274,0.676019, 4.1s) respectively. It’s been found that the training time of XGBoost-FA (external dimensionality reduction) is shorter than that of LightGBM (build-in dimensionality reduction). Considering (accuracy, training time) being (0.82, 3.1s) published on Kaggle, the algorithm (logogram as K2a) is better than the four XGBoost-FA and LightGBM in both training time and accuracy. However, K2a removes more than 50% samples with missing values and only performs binary classification. For multi-class classification or data with a large number of missing values, XGBoost-FA is more suggested if higher operational time is required, while LightGBM is preferred if higher predictive accuracy is required. With XGBoost-FA or LightGBM being employed in AI medical services, doctors are more productive in diagnosis and treatment due to much more data support and less workload. Both complement each other.