Fisher Scoring with Condition-Based Ensemble Supervised Learning Classification Technique for Prediction in PFZ

Abstract

Potential fishing zone (PFZ) alerts are critical in anticipating fishing places. Earlier PFZ predictions are based on NOAA’s advanced very high-resolution radiometer (AVHRR). To a significant degree, the expansion of the fishing industry may be attributed to the influence of research on fish growers, fishermen, fisheries planners, and managers. Artificial intelligence (AI) technologies are increasingly being used to improve the sustainability of smart fishing. While sustainability is frequently touted to be the intended consequence of AI applications, there is no data currently on how AI contributes to sustainable fishing. The purpose of this paper is to perform a feature selection using the fisher’s score (FS) technique to find the optimal features for final classification. Normalization is used as a preprocessing technique to remove missing and irrelevant data. Here, the collected features, financial derivatives, and geometrical features are used, which leads to poor classification accuracy for predicting the PFZ. Therefore, to improve the accuracy of the condition-based ensemble machine learning and deep learning classification technique (CECT), FS is used and provides the minimum number of features for classification. The experiment is carried out on collected data and tested with existing techniques in terms of accuracy, sensitivity, specificity, and F-measure. The simulation results proved that the proposed technique achieved 96.11% accuracy and 96% specificity compared to the FS technique.