The OWA Operator with Boxplot Method in Time Series

Torres-Martínez, Agustín; Gil‐Lafuente, Anna M.; Merigó, José M.

doi:10.1007/978-3-030-15413-4_2

Cited by 2 publications

(1 citation statement)

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“…On the other hand, the Inter Quartile Range (IQR) is a suitable measure for expressing dispersion when comparing different numerical image datasets [43]. Additionally, among the descriptive charts, the box plot can simultaneously display measures of concentration and disper- sion, including the lowest and highest values, the median, and the IQR components [44]. Figure 2 compares the box plot with the normal probability density curve to facilitate understanding of the box plot and its properties.…”

Section: A Criterion For Feature Varietymentioning

confidence: 99%

A new hierarchical algorithm based on CapsGAN for imbalanced image classification

Jabbari,

Bigdeli

2023

IET Image Processing

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Imbalanced image datasets consist of image datasets where there is a significant disparity in the number of samples across different classes. With imbalanced image datasets, learning algorithms often tend to be biased toward the majority class samples. This leads to poor classification of minority class samples as their training is not properly conducted. It becomes more complicated when the number of samples in the minority class is very low. In this paper, a novel hierarchical algorithm is proposed for generating new data using Capsule Generative Adversarial Networks (CapsGAN) to address the class imbalance problem in imbalanced image datasets. Unlike common GAN models, the proposed method incorporates an auxiliary CapsNet to identify high‐value images in both minority and majority classes. This identification is based on the ability to detect complex relationships between low‐level and high‐level features present in capsule networks. Furthermore, the proposed CapsGAN model is conditioned to generate minority class samples based on feature vectors of last capsule layer to achieve a more balanced image dataset. For evaluating the performance of the proposed model, an image dataset called CICS was collected and introduced. Extensive experiments were also conducted using various online image datasets from different domains, with varying numbers of classes and data sizes. The experimental results demonstrated that the proposed model can generate high‐quality samples in cases where the image dataset or the number of minority class samples is relatively small. Furthermore, the proposed model has maintained an accuracy of over 80% in an imbalanced ratio of 1:60.

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Section: A Criterion For Feature Varietymentioning

confidence: 99%