Classification and Outlier Detection Based on Topic Based Pattern Synthesis

Kokkula, Samrat; Musti, Narasimha Murty

doi:10.1007/978-3-642-39712-7_8

Cited by 4 publications

(1 citation statement)

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“…Another challenge of data mining is handling the problem of imbalanced data. A dataset is imbalanced when one class dataset has much fewer numbers of instances than the other class dataset [40]. Ref.…”

Section: Discussionmentioning

confidence: 99%

Improving the Accuracy of Predicting Bank Depositor’s Behavior Using a Decision Tree

Safarkhani

Moro

2021

Applied Sciences

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Telemarketing is a widely adopted direct marketing technique in banks. Since customers hardly respond positively, data prediction models can help in selecting the most likely prospective customers. We aim to develop a classifier accuracy to predict which customer will subscribe to a long-term deposit proposed by a bank. Accordingly, this paper focuses on a combination of resampling, in order to reduce the imbalanced data, using feature selection, to reduce the complexity of data computing and dimension reduction of inefficiency data modeling. The performed operation has shown an improvement in the performance of the classification algorithm in terms of accuracy. The experimental results were run on a real bank dataset and the J48 decision tree achieved 94.39% accuracy prediction, with 0.975 sensitivity and 0.709 specificity, showing better results when compared to other approaches reported in the existing literature, such as logistic regression (91.79 accuracy; 0.975 sensitivity; 0.495 specificity) and Naive Bayes classifier (90.82% accuracy; 0.961 sensitivity; 0.507 specificity). Furthermore, our resampling and feature selection approach resulted in improved accuracy (94.39%) when compared to a state-of-the-art approach based on a fuzzy algorithm (92.89%).

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Section: Discussionmentioning

confidence: 99%