Optimizing multi-classifier fusion for seabed sediment classification using machine learning

Abstract

Seabed sediment mapping with acoustical data and ground-truth samples is a growing field in marine science. In recent years, multi-classifier ensemble models have gained prominence for classification problems by combining several base classifiers. However, traditional ensemble methods do not consider the confidence scores of base classifiers, leading to suboptimal fusion when there are conflicting predictions. The current study introduces a novel optimization strategy that enhances the ensemble’s accuracy by constructing an ideal ensemble predicted probability matrix based on the fusion of predicted probabilities of the base classifiers, to improve seabed sediment mapping. The proposed approach not only addresses the limitations of traditional ensemble methods but also significantly increases the ensemble’s performance. The proposed approach demonstrates significant accuracy improvements. On the under-sampled dataset, it achieves 73.5% improvement compared to individual classifiers (random forest, decision tree, support vector machine), surpassing their respective accuracies. On the standard dataset, the ensemble model attains an accuracy of 79.1%, surpassing individual classifiers. Employing over-sampling techniques further elevates accuracy to 94.9%, exceeding the individual classifier performances. The proposed method is evaluated on acoustical data obtained from the Irish Sea. The proposed method outperforms base classifiers in terms of accuracy, F1 score, and the Kappa coefficient.