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The extreme learning machine based ensemble regression model was established to map the sensor data to clinical FMA scores while the RRelief algorithm was applied to find the optimal features subset.
Based on the same dataset, Riddick et al. built an ensemble regression model using Random Forest [ 10]; Lee et al. developed a co-expression extrapolation algorithm by comparing the differences of gene expression between sensitive and resistant cell lines [ 11].
Indeed, our results confirm this expectation: an ensemble logistic regression model trained on YEAST performs with AUC = 0.4833 in ECOLI, and an ensemble model trained in ECOLI performs with AUC = 0.5916 in YEAST.
We find that an ensemble logistic regression model trained on RELA obtains AUC = 0.996 when tested on NOTCH1, and likewise an ensemble model trained on NOTCH1 obtains AUC = 0.989 when tested on RELA.
Table 10 provides coefficients for the ensemble logistic regression model in the YEAST gold standard.
According to the comparison results, the hybrid metaheuristic regression model was better than the single and ensemble models in predicting the pitting corrosion risk (mean absolute percentage error=5.6%) and the marine corrosion rate (mean absolute percentage error = 1.26%).
We hypothesize that the high predictive performance of our regression model is due to properties of an antibody ensemble.
11 However, our ensemble scheme was outperformed by two other ensemble models, the ensemble of regression models (wpc-index of 0.562) and SVMs (ensemble model 1, wpc-index of 0.554).
As in Experiment 2, we used a multilevel logistic regression model to determine the impact of the colocation of an ensemble track and an oil rig.
For each gold standard, Table 9 compares the AUC achieved by the best individual causal orientation method to the AUC achieved by the ensemble method, which combines the predictions of all 11 methods using a logistic regression model.
It is a regression model.
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