Visualization and Analysis in Bank Direct Marketing Prediction

Abu-Srhan, Alaa; Alhammad, Bara’a; zghoul, Sanaa Al; Al-Sayyed, Rizik

doi:10.14569/ijacsa.2019.0100785

Cited by 4 publications

(3 citation statements)

References 22 publications

(28 reference statements)

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“…Singh et al [38] compared the performance of LR, SVM, RF, and DT models for the prediction task and suggested that the RF model achieved the best result. A similar study by Hou et al [39] compared the performance of NB, DT, RF, SVM, and NN and concluded that RF achieved the highest accuracy while NN produced the highest sensitivity value, suggesting its robustness in the prediction task. However, they did not explicitly use any feature selection or class balancing technique.…”

Section: Feature Selectionmentioning

confidence: 88%

Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

Kaisar,

Rashid,

Chowdhury

et al. 2024

Big Data Min. Anal.

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Telemarketing is a well-established marketing approach to offering products and services to prospective customers. The effectiveness of such an approach, however, is highly dependent on the selection of the appropriate consumer base, as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested ones. The introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base, and the existing literature in this direction shows promising results. However, the selection of influential features and the construction of effective learning models for improved performance remain a challenge. Furthermore, from the modelling perspective, the class imbalance nature of the training data, where samples with unsuccessful outcomes highly outnumber successful ones, further compounds the problem by creating biased and inaccurate models. Additionally, customer preferences are likely to change over time due to various reasons, and/or a fresh group of customers may be targeted for a new product or service, necessitating model retraining which is not addressed at all in existing works. A major challenge in model retraining is maintaining a balance between stability (retaining older knowledge) and plasticity (being receptive to new information). To address the above issues, this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately. A novel online learning method is proposed for model retraining when new samples are available over time. This newly introduced method equips the proposed approach to deal with dynamic data, leading to improved readiness of the proposed model for practical adoption, and is a highly useful addition to the literature. Extensive experiments with real-world data show that the proposed approach achieves excellent results in all cases (e.g., 98.6% accuracy in classifying customers) and outperforms recent competing models in the literature by a considerable margin of 3% on a widely used dataset.

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Section: Feature Selectionmentioning

confidence: 88%

Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

Kaisar,

Rashid,

Chowdhury

et al. 2024

Big Data Min. Anal.

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“…(9): (9) where density is inversely proportional to the sparsity hence, inverse of density will be equal to the sparsity factor that can be measured as Eq. (9): (10) After measuring sparsity for each filtered minority cluster, we can take sum of all the sparsity measures and this sparsity sum is then transformed into the sampling weights as Eq. (11):…”

Section: Synthetic Instance Generationmentioning

confidence: 99%

“…At present, data mining tasks involve large amount of data which is complex and embedded with noise; hence techniques used for information extraction are needed to be efficient and effective decisions making [1] [2]. In data mining, classification is the most commonly performed data analysis task in realworld applications including medical, engineering, and business [3][4] [5]; i.e., cancer prediction [6], face detection [7], software fault detection [8] [9], bankruptcy prediction [10] [11], fraud detection [12]. Majority of classification algorithms consider that the given dataset has the proportional instances among the classes, and these algorithms are not intelligent enough to detect the inappropriate distribution of instances.…”

Section: Introductionmentioning

confidence: 99%

Improved Adaptive Semi-Unsupervised Weighted Oversampling using Sparsity Factor for Imbalanced Datasets

Ali¹,

Najib²,

Hussain³

2019

IJACSA

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With the incredible surge in data volumes, problems associated with data analysis have been increasingly complicated. In data mining algorithms, imbalanced data is a profound problem in machine learning paradigm. It appears due to desperate nature of data in which, one class with a large number of instances presents the majority class, while the other class with only a few instances is known as minority class. The classifier model biases towards the majority class and neglects the minority class which may happen to be the most essential class; resulting into costly misclassification error of minority class in real-world scenarios. Imbalanced data problem is significantly overcome by using re-sampling techniques, in which oversampling techniques are proven to be more effective than undersampling. This study proposes an Improved Adaptive Semi Unsupervised Weighted Oversampling (IA-SUWO) technique with sparsity factor, which efficiently solves between-the-class and within-the-class imbalances problem. Along with avoiding over-generalization, overfitting problems and removing noise from the data, this technique enhances the number of synthetic instances in the minority sub-clusters appropriately. A comprehensive experimental setup is used to evaluate the performance of the proposed approach. The comparative analysis reveals that the IA-SUWO performs better than the existing baseline oversampling techniques.These methods manipulate the data externally by balancing the distribution of samples among the classes [17] [15].

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