Advancing Cancer Document Classification with R andom Forest

Che, Chang; Hu, Hao; Zhao, Xinyu; Li, Shulin; Lin, Qunwei

doi:10.54097/ajst.v8i1.14333

Cited by 12 publications

(9 citation statements)

References 13 publications

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“…Because it is extremely difficult to obtain labels of fraudulent transactions, some scholars regard fraudulent samples as outliers and separate them from normal samples by anomaly detection technology. Van et al [9][10]. H used unsupervised anomaly detection technology to identify fraud samples of medical insurance claims, and the experimental results showed that potential new fraud patterns could be detected through anomaly detection technology.…”

Section: Related Workmentioning

confidence: 99%

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

Zheng,

Yang,

Xin

et al. 2024

FCIS

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Because of its close relationship with information technology, every change in the field of information technology will have an important impact on the means, time and space distribution of the final fraud of the credit card network. The rapid development and popularization of artificial intelligence (AI) software based on Deepfake technology has greatly promoted the intelligent transformation of credit card network fraud. Through the use of advanced hardware and software equipment, criminals have iterated on their own fraud tools, from the use of traditional phone calls and text messages to the use of the latest AI software. Therefore, for the problem of credit card fraud detection with missing data set labels and highly unbalanced category distribution, A credit card approval anomaly detection model DES-HBOS (Dynamic Ensemble Selection based on Histogram-based Outlier Score) is proposed. Firstly, the unsupervised anomaly detection algorithm is used to construct the false label of the training set customer. Then, the customer capability area to be measured is determined, and the classifier performance is evaluated according to Pearson correlation coefficient. Finally, a set of optimal classifiers is selected to integrate the test customers. Experiments on real credit card customer data sets show that DES-HBOS has a higher Recall and can identify more fraudulent customers than other 6 classical anomaly detection models. Comparative experiments were carried out on 4 unbalanced data sets, and the experimental results showed that DES-HBOS had stronger anomaly detection ability than HBOS.

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Section: Related Workmentioning

confidence: 99%

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

Zheng,

Yang,

Xin

et al. 2024

FCIS

View full text Add to dashboard Cite

show abstract

“…Different from innovation from model itself, lots of works proves a better feature selection will boost performance compare with the classic approach [4]: [7] proposed a method which use multiresolution wavelets and Zernike moments to get a better representation; [8] utilize Gabor wavelet and locality sensitive discriminant analysis in feature selection process. [9] introduce a Cross-correlation analysis in both spatial and wavelet domains before using PCA as feature selection method.…”

Section: Related Workmentioning

confidence: 99%

“…[8] introduce organize the data as a graph structure to apply graph convolution. Considering that healthcare datasets are generally less public and smaller in size, [9] proposes new data enhancement schemes to improve data quality.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Model Aids Breast Cancer Detection

Zhang,

Cai,

Cai

et al. 2023

FCIS

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Breast cancer, a lumpy nodule or granular calcified tissue caused by cancerous changes in chest tissue, has become one of the most prevalent cancers. Due to the location and structure of the tumor, it can be detected directly by ultrasound or X-ray and is less likely to spread to other parts of the body than tumors in other parts of the body. Considering the huge number of sick people, the resources required for a full census would be enormous, but thanks to the rapid development of medical image processing technology in recent years, assisted diagnosis through deep learning models has gradually become more widely accepted. For detection models, higher accuracy means lower misdiagnosis rates and timely treatment for patients. Therefore, in this paper, we first specify the diagnose as a binary classification problem and then introduce a new pooling scheme and training method to achieve better results compared to the traditional network backbone in the past.

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“…At present, the radiative transfer model RF is used to characterize the climate effect of greenhouse gases, indicating the general trend of climate change. When RF>0, it will warm the ground and troposphere, and when RF0, it will cool the ground and troposphere [4]. 1 In terms of radiative forcing methods for calculating CO, the internationally recognized and widely used atmospheric radiation models are AER's atmospheric radiation model Fu-Liou model MODTRAN5, SBDART, SHDOM, etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Atmospheric Carbon Dioxide Radiative Transfer Model based on Machine Learning

Duan,

Liu,

et al. 2024

FCIS

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Global warming is caused by the increasing amount of greenhouse gases in the atmosphere. The Kyoto Protocol to the United Nations Framework Convention on Climate Change defines carbon dioxide (CO), methane (CH:), nitrous oxide (N0) fluorocarbons (HFC), holocarbon (PFC), and sulfur hexafluoride (SF.) Six gases are divided into the main greenhouse gases, of which CO, the largest proportion of emissions, is an important man-made greenhouse gas." As the main gas affecting the global greenhouse effect (75%), the amount of CO in the atmosphere has increased from 315 ppm in 1958 to 417 ppm in 2022, and global annual CO emissions have increased from 27 Pg to 49 Pg% over the past 40 years. Therefore, under the background of deep reinforcement learning, it is a problem that all countries pay more attention to predict the concentration emission of carbon dioxide to cope with the severe situation of climate warming. In order to obtain the radiative forcing value under the influence of carbon dioxide concentration, a simplified net radiative flux model is established. Based on the radiative transfer equation, this model can calculate the net radiative flux and radiative forcing of each layer of the atmosphere due to changes in carbon dioxide concentration. The results are compared with the RRTMG-LW long-wave radiative transfer model of American Center for Atmospheric and Environmental Research (AER) under the same factor, and the error is less than 1%.

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Advancing Cancer Document Classification with R andom Forest

Cited by 12 publications

References 13 publications

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

The Credit Card Anti-fraud Detection Model in the Context of Dynamic Integration Selection Algorithm

Deep Learning Model Aids Breast Cancer Detection

Prediction of Atmospheric Carbon Dioxide Radiative Transfer Model based on Machine Learning

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