Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Lin, Hsien-I; Nguyen, Mihn Cong

doi:10.3390/app10030973

Cited by 18 publications

(11 citation statements)

References 38 publications

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“…Imbalance data remains a key challenge against classification models [15,18]. The majority of literature considered re-sampling approaches, i.e., both over-sampling and under-sampling, to alleviate degradation due to the issue of imbalanced data [1,17,19,33,37].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…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: Theoretical Backgroundmentioning

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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“…To force the model to more reliably predict the minority class, you can up-sample that class through synthetic data generation. Alternatively, if your sample size is sufficient, you may down sample the majority class [ 181 ] to better balance your input data, although care should be taken not to exclude relevant subgroups. An important distinction is that while synthetic or resampled can be applied to model training data, it is generally not acceptable to include synthetic or resampled data in testing datasets.…”

Section: Data Extraction and Preprocessingmentioning

confidence: 99%

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

Pettit

Fullem

Cheng

et al. 2021

Emerging Topics in Life Sciences

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AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.

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“…In another view, most of the sampling methods rely on balancing the mini-batches of training samples [34], [35], [36]; however, lots of graph neural networks, including GCN, should be trained in the full-batch mode [37], [38]. Even in solving class imbalance for non-graph datasets, oversampling the minority class(es) causes the overfitting to the duplicate samples drawn from the minor class(es), and under-sampling the majority class(es) causes the exclusion of required samples for discrimination [39]. These weaknesses lead many methods to costsensitive approaches.…”

Section: Related Workmentioning

confidence: 99%

RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data

Ghorbani,

Kazi,

Baghshah

et al. 2021

Preprint

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Disease prediction is a well-known classification problem in medical applications. Graph neural networks provide a powerful tool for analyzing the patients' features relative to each other and redefine the problem as a node classification task. Recently, Graph Convolutional Networks (GCNs) have particularly been studied in the field of disease prediction. Due to the nature of such medical datasets, the class imbalance is a familiar issue in the field of disease prediction, where the distribution of classes is skewed. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es) and neglect the samples in the minor class(es). On the other hand, it should be noted that the correct diagnosis of the rare true-positive cases among all the patients is vital in the healthcare system. In conventional methods, such imbalance is tackled by assigning appropriate weights to classes in the loss function; however, this solution is still dependent on the relative values of weights, sensitive to outliers, and in some cases biased towards the minor class(es). In this paper, we propose Re-weighted Adversarial Graph Convolutional Network (RA-GCN) to enhance the performance of the graph-based classifier and prevent it from emphasizing the samples of any particular class. This is accomplished by automatically learning to weigh the samples of the classes. For this purpose, a graph-based network is associated with each class, which is responsible for weighing the class samples and informing the classifier about the importance of each sample. Therefore, the classifier adjusts itself and determines the boundary between classes with more attention to the important samples. The parameters of the classifier and weighing networks are trained by an adversarial approach. At the end of the adversarial training process, the boundary of the classifier is more accurate and unbiased. We show experiments on synthetic and three publicly available medical datasets. Our results demonstrate the superiority of RA-GCN compared to recent methods by an average of 12% on F1-score for minor class and 6% on macro-averaged F1-score on all three datasets. The detailed analysis of our method is provided as quantitative and qualitative experiments on synthetic datasets.

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Boosting Minority Class Prediction on Imbalanced Point Cloud Data

Cited by 18 publications

References 38 publications

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

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

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction

RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data

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