Adaptive Continuous Feature Binarization for Tsetlin Machines Applied to Forecasting Dengue Incidences in the Philippines

The Tsetlin Machine (TM) is a recent interpretable machine learning algorithm that requires relatively modest computational power, yet attains competitive accuracy in several benchmarks. TMs are inherently binary; however, many machine learning problems are continuous. While binarization of continuous data through brute-force thresholding has yielded promising accuracy, such an approach is computationally expensive and hinders extrapolation. In this paper, we address these limitations by standardizing features to support scale shifts in the transition from training data to real-world operation, typical for e.g. forecasting. For scalability, we employ sampling to reduce the number of binarization thresholds, relying on stratification to minimize loss of accuracy. We evaluate the approach empirically using two artificial datasets before we apply the resulting TM to forecast dengue outbreaks in the Philippines using the spatiotemporal properties of the data. Our results show that the loss of accuracy due to threshold sampling is insignificant. Furthermore, the dengue outbreak forecasts made by the TM are more accurate than those obtained by Support Vector Machines (SVMs), Decision Trees (DTs), and several multi-layered Artificial Neural Networks (ANNs), both in terms of forecasting precision and Fl-score.