Dimensionality reduction

Random forest

Linguistics

Engineering

Philosophy

Sobol sequence

Selection (genetic algorithm)

Data mining

Computer science

Ranking (information retrieval)

Pattern recognition (psychology)

Feature selection

Feature (linguistics)

Electronic engineering

Artificial intelligence

Sensitivity (control systems)

Model selection

Machine learning

Applied Soft Computing

Feature selection (FS) is vitally important for determining the optimum subsets of features with effective information and maximizing the model accuracy. This study proposes a novel FS method based on global sensitivity analysis (GSA) for effectively determining the most relevant feature subsets and improving prediction performance of machine learning (ML)based models. Feature ranking is determined based on the results obtained from three global sensitivity analysis (GSA) including Pearson, Sobol’ and PAWN. This novel GSA-based FS method is applied to engineering practice with the combination of ML algorithm random forest (RF) to predict tunnelling-induced settlement prediction model. Meanwhile, the feature extraction method principle component analysis (PCA) is also used to develop RF-based model for comparing the performance of proposed GSA-based FS method. The results indicate the novel GSA-based FS method effectively determines the significance of input variables. The prediction performance of RF-based model with the integration of GSA-based FS methods is enhanced dramatically, and obviously outperforms the model with the integration of PCA-based dimensionality reduction method.

A novel feature selection method based on global sensitivity analysis with application in machine learning-based prediction model