Unlocking the potential of Naive Bayes for spatio temporal classification: a novel approach to feature expansion

Prediction processes in areas ranging from climate and disease spread to disasters and air pollution rely heavily on spatial-temporal data. Understanding and forecasting the distribution patterns of disease cases and climate change phenomena has become a focal point of researchers around the world. Machine learning models for prediction can generally be classified into 2: based on previous patterns such as LSTM and based on causal factors such as Naive Bayes and other classifiers. The main drawback of models such as Naive Bayes is that it does not have the ability to predict future trends because it only make predictionsin the present time. In this study, we propose a novel approach that makes the Naive Bayes classifier capable of predicting future classification. The process of expanding the dimension of the feature matrix based on historical data from several previous time periods is performed to obtain a long-term classification prediction model using Naive Bayes. The case studies used are the prediction of the distribution of the annual number of dengue fever cases in Bandung City and the distribution of monthly rainfall in Java Island, Indonesia. Through rigorous testing, we demonstrate the effectiveness of this Time-Based Feature Expansion approach in Naive Bayes in accurately predicting the distribution of annual dengue fever cases in 30 sub-districts in Bandung City and monthly rainfall in Java Island, Indonesia with with both accuracy and F1-score reaching more than 97%.