The prediction of bankruptcy using fuzzy classifiers

Real-world databases are highly susceptible to noisy, missing, and inconsistent data due to their typically huge size, which is a prevailing problem in data analysis. The easiest way to handle such data sets in classification is to discard data with missing and extreme values. Since this complete case approach may result in a loss of valuable information and reduced data set size, preprocessing techniques are used in this paper. These techniques include data cleaning, data transformations and data reduction. A new feature selection for data reduction is introduced, which uses the fast fuzzy clustering algorithm in classification problems. The experiments show the advantages of the proposed methods for data preprocessing in a real world problem: the prediction of bankruptcy. The data set used in this study has missing values and extreme values. The data set also presents a much smaller bankruptcy class than the not bankruptcy class