Analysis and Comparison of Data Mining Tools and Techniques for Classification of Banknote Authentication

Abstract

Banknote authentication is very important and challenging task for every banks and financial sectors to secure the information from unauthorized person. Banknote authentication avoids fraud or misuses of financial system and protects the financial data from unauthorized users. Classification play very major role to classify the authentic and counterfeit notes. In this paper we have presented the classification accuracy of different classifiers and compared the accuracy of classifiers using three data mining tools. In first case compared the accuracy of classifiers using Rapid miner and WEKA (Waikato Environment for Knowledge Analysis) data mining tools where Random Forest gives better accuracy as 99.30% in case of WEKA data mining tool. In second case compared the accuracy of classifiers using Rapid miner and Tanagra data mining tool where K-NN gives 99.56% of accuracy in Tanagra data mining tool. Finally compared the classification accuracy using WEKA and Tanagra data mining tool where MLP gives 99.1% of accuracy in case of WEKA data mining tool.