Classical conditioning, the association of such an event with another desired event resulting in behavior, is one of the easiest to understand processes of learning. It is the process of learning to associate a particular thing in our environment with a prediction of what will happen next.
And from the viewpoint of explainable deep learning the hierarchical cluster structure constructed through HC-DNN can represent the relationship of fraud types. Theories of learning Classical Conditioning Operant Conditioning Cognitive Theory. As a result of evaluating the performance of fraud detection by cross validation, the results of the proposed method show higher performance than those of conventional methods.
HC-DNN has the advantage of improving the performance and providing the explanation about the relationship of fraud types. The proposed method, Hierarchical Clusters-based Deep Neural Networks (HC-DNN) utilizes anomaly characteristics of hierarchical clusters pre-trained through an autoencoder as the initial weights of deep neural networks to detect various frauds. This paper proposes a novel method using hierarchical clusters based on deep neural networks in order to detect more detailed frauds, as well as frauds of whole data in the work processes of job placement. Previous studies for fraud detection have limited the performance enhancement because they have learned the fraud. Fraud is an inhibitory factor to accurate appraisal in the evaluation of an enterprise, and it is economically a loss factor to business. Fraud detection is becoming an integral part of business intelligence, as detecting fraud in the work processes of a company is of great value.
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