Optimization of urban land cover classification using an improved Elephant Herding Optimization algorithm and random forest classifier

Abstract

ABSTRACT This paper aims to provide a novel approach for improving urban land cover classification accuracy, which combines the Elephant Herding Optimization (EHO) algorithm as a meta-heuristic optimization method with the Random Forest (RF) classifier. The proposed approach involves both classifier hyperparameter tuning and feature selection for a data set of a selected urban area. The EHO and the RF algorithms were both utilized in a hybrid system (EHO-RF) to find an optimal classification model with a tuned set of hyperparameters and features (predictor variables). EHO-RF model tuning was followed by backward feature elimination using RF, which further reduced data dimensionality. This work also combines two well-known optimization and feature selection algorithms to enhance accuracy assessment of the proposed approach. Grid search optimization was combined with Variable Selection Using Random Forests (VSURF) algorithm to build an optimization system (Grid-VSURF) that is similar to the workflow of EHO-RF. Objective functions for both Grid-VSURF and EHO-RF were designed to perform 10-fold cross-validation to reduce over-fitting. An area in Deerfield Beach city, Florida, USA, was selected as the study area representing an urban land cover. The testing dataset used in this study represents a performance benchmarking dataset, which has been utilized in many studies. EHO-RF results showed a significant improvement with an overall map accuracy of 83.83%, compared with Grid-VSURF results with an overall map accuracy of 74.75%. The number of input features was significantly reduced by both approaches with 82.30% reduction using EHO-RF with backward elimination and 89.70% reduction using Grid-VSURF.