A statistical perspective on knowledge discovery in databases
Advances in knowledge discovery and data mining
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using Rule Sets to Maximize ROC Performance
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
International Journal of Approximate Reasoning
A lot of randomness is hiding in accuracy
Engineering Applications of Artificial Intelligence
Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms
International Journal of Approximate Reasoning
Initial population construction for convergence improvement of MOEAs
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving Compact and Interpretable Takagi–Sugeno Fuzzy Models With a New Encoding Scheme
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
A hybrid coevolutionary algorithm for designing fuzzy classifiers
Information Sciences: an International Journal
A Minimum-Risk Genetic Fuzzy Classifier Based on Low Quality Data
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
IEEE Transactions on Fuzzy Systems
A dynamically constrained multiobjective genetic fuzzy system for regression problems
IEEE Transactions on Fuzzy Systems
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
A double axis classification of interpretability measures for linguistic fuzzy rule-based systems
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
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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.