Cost-sensitive ensemble of stacked denoising autoencoders for class imbalance problems in business domain

Wong, Man Leung; Seng, Kruy; Wong, Pak Kan

doi:10.1016/j.eswa.2019.112918

Cited by 71 publications

(45 citation statements)

References 36 publications

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“…Data-driven decision-making process [1][2][3][4][5][6][7] has been playing an essential part of the critical responses to the stringent business environment [4,[8][9][10][11][12][13]. Identifying profitable or costly customer is crucial for businesses to maximize the returns, preserve a long-term relationship with the customers, and sustain a competitive advantage [14].…”

Section: Introductionmentioning

confidence: 99%

“…The nature of bank marketing data presents a challenge that is facing the researchers in business analytics [3,7,10]. The low volume of the potential target/important customer data (i.e., imbalanced data distribution) is a major challenge in extracting the latent knowledge in banks marketing data [1,3,10]. There is still an insisting need for handling the imbalanced dataset distribution reliably [15][16][17]; commonly used approaches [1,15,16,[18][19][20][21] impose processing overhead or lead to loss of information.…”

Section: Introductionmentioning

confidence: 99%

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Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

et al. 2020

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The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassification errors were minimized to 0.192, 0.229 of Type I & Type II Errors, respectively. In summary, an interesting Meta-Cost method improved the performance of the prediction model without imposing significant processing overhead or altering original data samples.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

et al. 2020

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“…[N] then 13: for n = 0 to n < (N − 1) do 14: // (N-1) because, the add index is deleted (Step 4). 15:…”

Section: Init( )mentioning

confidence: 99%

“…Machine learning and deep learning algorithms are strongly affected by the class imbalance problem [11][12][13][14][15]. The latter refers to some difficulties that appear when the number of samples in one or more classes in the dataset is fewer than another class (or classes), thereby producing an important deterioration of the classifier performance [16].…”

Section: Introductionmentioning

confidence: 99%

“…Another way to deal with the class imbalance problem has been through the Cost Sensitive (CS) approach [34], which has become an important topic in deep learning research in recent years [13][14][15]35]. CS considers the costs associated with misclassifying samples; i.e., it uses different cost matrices describing the costs of misclassifying any particular data sample [29].…”

Section: Introductionmentioning

confidence: 99%

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Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem

et al. 2020

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The class imbalance problem has been a hot topic in the machine learning community in recent years. Nowadays, in the time of big data and deep learning, this problem remains in force. Much work has been performed to deal to the class imbalance problem, the random sampling methods (over and under sampling) being the most widely employed approaches. Moreover, sophisticated sampling methods have been developed, including the Synthetic Minority Over-sampling Technique (SMOTE), and also they have been combined with cleaning techniques such as Editing Nearest Neighbor or Tomek's Links (SMOTE+ENN and SMOTE+TL, respectively). In the big data context, it is noticeable that the class imbalance problem has been addressed by adaptation of traditional techniques, relatively ignoring intelligent approaches. Thus, the capabilities and possibilities of heuristic sampling methods on deep learning neural networks in big data domain are analyzed in this work, and the cleaning strategies are particularly analyzed. This study is developed on big data, multi-class imbalanced datasets obtained from hyper-spectral remote sensing images. The effectiveness of a hybrid approach on these datasets is analyzed, in which the dataset is cleaned by SMOTE followed by the training of an Artificial Neural Network (ANN) with those data, while the neural network output noise is processed with ENN to eliminate output noise; after that, the ANN is trained again with the resultant dataset. Obtained results suggest that best classification outcome is achieved when the cleaning strategies are applied on an ANN output instead of input feature space only. Consequently, the need to consider the classifier's nature when the classical class imbalance approaches are adapted in deep learning and big data scenarios is clear.

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