COMBINING ADABOOST WITH PREPROCESSING ALGORITHMS FOR EXTRACTING FUZZY RULES FROM LOW QUALITY DATA IN POSSIBLY IMBALANCED PROBLEMS
An extension of the Adaboost algorithm for obtaining fuzzy rule-based systems from low quality data is combined with preprocessing algorithms for equalizing imbalanced datasets. With the help of synthetic and real-world problems, it is shown that the performance of the Adaboost algorithm is degraded in presence of a moderate uncertainty in either the input or the output values. It is also established that a preprocessing stage improves the accuracy of the classifier in a wide range of binary classification problems, including those whose imbalance ratio is uncertain.
low quality data genetic fuzzy systems boosting imbalanced classification