COMBINING ADABOOST WITH PREPROCESSING ALGORITHMS FOR EXTRACTING FUZZY RULES FROM LOW QUALITY DATA IN POSSIBLY IMBALANCED PROBLEMS

ANA PALACIOS, LUCIANO SANCHEZ, INES COUSO
2012 International Journal of Uncertainty Fuzziness and Knowledge-Based Systems  
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
more » ... range of binary classification problems, including those whose imbalance ratio is uncertain.
doi:10.1142/s0218488512400156 fatcat:2tvxa2z7mzhg7jmzc55xa6m674