A Novel Tsetlin Automata Scheme to Forecast Dengue Outbreaks in the Philippines

Being capable of online learning in unknown stochastic environments, Tsetlin Automata (TA) have gained considerable interest. As a model of biological systems, teams of TA have been used for solving complex problems in a decentralized manner, with low computational complexity. For many domains, decentralized problem solving is an advantage, however, also may lead to coordination difficulties and unstable learning. To combat this negative effect, this paper proposes a novel TA coordination scheme designed for learning problems with continuous input and output. By saving and updating the best solution that has been chosen so far, we can avoid having the overall system being led astray by spurious erroneous actions. We organize this process hierarchically by a principal-teacherclass structure. We further propose a binary representation of continuous actions (coefficients). Each coefficient in the cost function is represented by 8 TA. TA teams at different classes produce different solutions. They are trained to find the global optimum with the help of their own best and the overall best solutions. The proposed algorithm is tested first with an artificial dataset and later used to forecast dengue haemorrhagic fever in the Philippines. Results of the novel procedure are compared with results from two traditional TA approaches. The training error of the novel TA scheme is lower approx. 50 and 62 times compared to the considered two traditional Tsetlin Automata approaches and testing error is approx. 31 and 21 times lower for the new scheme. These improvements not only highlight the effectiveness of the proposed scheme, but also the importance of old, simple, yet powerful concepts in the Artificial Intelligence techniques.