![]() An integrated hybrid model consisting of Bi-Directional Gated Recurrent Units and Bi-Directional Long-Term Short-Term Memory classifies the consumers, efficiently. ![]() Moreover, to carry out affine training of the model, balanced data are inputted in order to mitigate class imbalance issues. In addition, to enhance the detection of the classifier, abstract features are engineered using a stochastic feature engineering mechanism. Furthermore, a K-means minority oversampling technique is used to tackle the class imbalance issue. Theft data variants are benign class appertaining data samples which are modified and manipulated to synthesize malicious samples. In order to cope with a Theft Case scenario, theft data is ascertained and synthesized randomly by using six theft data variants. To tackle the problem of the defused data, a Tomek Links technique targets the cross-pair majority class and is removed, which results in an affine-segregated decision boundary. ![]() ![]() Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples’ nature. In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. ![]()
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