چکیده مقاله
Fraud detection is one of the applications of data mining techniques which is used in banks and credit institutions Preparing a proper model to detect the fraud requires a more effective feature selection Feature selection is a common technique in pre processing which is used to reduce the dimensions of the data set The current paper presents a hybrid approach to detect the fraud in credit cards So, firstly, the primary features of the dataset are determined by the genetic algorithm followed by learning the model through hybridizing three different Type of neural network algorithms Three mentioned algorithms are hybridized by the majority of votes, a weighing approach In this approach the results and coefficients obtained by each neural network algorithm are voted to make the final output The obtained results show that the mentioned technique, comparing the AFDM and AIS techniques, improves the cost and accuracy of prediction considerably We have obtained an accuracy of 97 972 % by implementation of the aforementioned technique for this dataset Similarly, the cost obtained through this study, has decreased almost 72% and 9% comparing with AIS and AFDM, respectively
کلیدواژهها
نویسندگان
Solmaz Karimi
Department of Computer, Faculty of Mechatronic, Karaj Branch, Islamic Azad University, Karaj, Iran
Majid Khalilian
Department of Computer, Faculty of Mechatronic, Karaj Branch, Islamic Azad University, Karaj, Iran
Alireza NikravanShalmani
Department of Computer, Faculty of Mechatronic, Karaj Branch, Islamic Azad University, Karaj, Iran
شیوه ارجاع
Karimi, Solmaz and Khalilian, Majid and NikravanShalmani, Alireza,1395,Fraud Detection Using Neural Networks,3rd National Congress of Electrical and Computer Engineering of Iran,Tehran
ارائهشده در
مجموعه مقالات سومین کنفرانس سراسری نوآوری های اخیر در مهندسی برق و کامپیوتر19 شهریور 1395 · تهران