Bank Note Authentication Using Decision Tree rules and Machine Learning Techniques

被引:0
|
作者
Kumar, Chhotu [1 ]
Dudyala, Anil Kumar [1 ]
机构
[1] Natl Inst Technol, Dept Comp Sci & Engn, Patna, Bihar, India
关键词
Banknote Authentication; Multilayer Perceptron; Radial Basis Function; Probabilistic Neural Network; Decision Tree; Decision tree rules; Naive Base; SUPPORT VECTOR MACHINES; RADIAL BASIS FUNCTIONS; BANKRUPTCY PREDICTION; NEURAL-NETWORKS; ROC CURVES; PERFORMANCE; REGRESSION;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Banknotes are currencies used by any nation to carry-out financial activities and are every countries asset which every nation wants it (bank-note) to be genuine. Lot of miscreants induces fake notes into the market which resemble exactly the original note. Hence, there is a need for an efficient authentication system which predicts accurately whether the given note is genuine or not. Exhaustive experiments have been conducted using different machine learning techniques and found that Decision tree and MLP techniques are effective for banknote authentication which efficiently classifies a given banknote data. The rules given by Decision Tree are also tested and found that they are accurate enough to be used for prediction.
引用
收藏
页码:310 / 314
页数:5
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