Fraud Detection Using Multi-layer Heterogeneous EnsembleMethod

Abstract

Fraudulent detection is a large number of exercises that try to keep cash or property out of the way. Fraud surveillance is used in many businesses such as banking or security. At the bank, misrepresentation may involve producing checks or using a Credit Card taken. Different types of robberies can include misfortune or create a problem with the expectation of only a paid Layer Ensemble Method running other AI fields including collecting learning. Recently, there have been one deep group models deployed with a large number of classifiers in each layer. These models, as a result, require a much larger calculation. In addition, the deep integration models are available that use all the separating elements including the unnecessary ones that can reduce the accuracy of the group. In this experiment, we propose a multi-layered learning structure called the Two-Layer Ensemble System to address the issue of definition. The proposed framework is working with a number of weird filters to get the troupe jumper sity, in these lines being a technology in the use of equipment.