Detection of Safe and Harmful Bioaerosols by Means of Fuzzy Classifiers

This paper aims to create a fuzzy classifier (FC) to be used in a recently developed bioaerosol detector. The main requirements for FC are high true positive (TP) rate, low false positive (FP) rate, and interpretability, which is measured by transparency of fuzzy partition. Due to the contradicting nature of the above requirements, FCs are identified by hybrid genetic fuzzy system (GFS), which initializes the population using decision trees (DTs) and simplification operations. Then, a multiobjective evolutionary algorithm (MOEA) is run in order to find a Pareto-optimal set of FCs. During MOEA optimization, heuristic rule and rule condition removal is applied to keep the rule base consistent. Real-world bioaerosol data, collected from Ume̊a trial field, Sweden, and from laboratory of Finnish Defense Forces Technical Research Center, were used to validate the proposed GFS. By means of it, a widely spread set of interpretable and accurate FCs was obtained. Moreover, an FC based on this project was installed into the bioaerosol detector and the preliminary tests proved its capability in distinguishing between safe and harmful bioaerosols.