Lossy Predictive Models for Accurate Classification Algorithms

Recent years have witnessed an upsurge of interest in lossy compression due to its potential to significantly reduce data volume with adequate exploitation of the spatiotemporal properties of IoT datasets. However, striking a balance between compression ratios and data fidelity is challenging, particularly when losing data fidelity impacts downstream data analytics noticeably. In this paper, we propose a lossy prediction model dealing with binary classification analytics tasks to minimize the impact of the error introduced due to lossy compression. We specifically focus on five classification algorithms for frost prediction in agricultural fields allowing preparation by the predictive advisories to provide helpful information for timely services. While our experimental evaluations reaffirm the nature of lossy compressions where allowing higher errors offers higher compression ratios, we also observe that the classification performance in terms of accuracy and F-1 score differs among all the algorithms we evaluated. Specifically, random forest is the best lossy prediction model for classifying frost. Lastly, we show the robustness of the lossy prediction model based on the data fidelity in prediction performance.