An Effective Hybrid Fuzzy Classifier Using Rough Set Theory for Outlier Detection in Uncertain Environment

Abstract

Data mining is the process that is used to extract the meaningful information from the large size dataset. The effective dataset utilization depends on the proper classification of outliers. The main objective of the outlier detection is to extract the abnormal data with inconsistency. The information collection from the different mechanisms is uncertain in nature. The data uncertainty causes the knowledge imperfections namely, vagueness and indiscernibility. The proposed research work implements an efficient fuzzy–rough set classifier for an outlier detection with less computational complexity. The fuzzy logic utilization and the fix of abnormal data easily determine the outliers in the large size database. The proposed classifier is compared with the existing classification methods with performance parameters like average running time, average execution time, execution time, false negative, false positive, true negative, true positive, precision, recall, and accuracy. The comparative analysis depicts the effectiveness of classifier in outlier detection.