Developing a bioaerosol detector using hybrid genetic fuzzy systems

The aim of this work is to develop a model, which works as a reasoning mechanism in a bioaerosol detector. Ability to distinguish between safe and harmful aerosols is one of its main requirements. Instead of commonly used misclassification rate as a metric of accuracy, true positive (TP) and false positive (FP) rates are used because of the uneven misclassification costs and class distributions of the collected data. Interpretability of the model builds up the confidence for the developed model and enables its adjustment in cases when bioaerosol detector is further developed. Thus, it is another crucial requirement for the model. Clearly, the objectives are contradicting and therefore multiobjective evolutionary algorithms (MOEAs) are applied to find tradeoff models. Fuzzy classifiers (FCs) are selected as a model type because their linguistic rules are intuitive to human beings. FCs are identified by hybrid genetic fuzzy system (GFS) which initializes the population adequately using decision trees (DTs) and simplification operations. During MOEA optimization transparency of fuzzy partition is used as a metric of interpretability and TP and FP rates as metrics of accuracy. Heuristic rule and rule condition removal is applied to offspring population in order to keep the rule base consistent. The identified FCs are highly comprehensible yet accurate and their linguistic rules provide valuable insights for further development of bioaerosol detector.