[Retracted] Construction of Financial Management Early Warning Model Based on Improved Ant Colony Neural Network

Wang, Meiluan

doi:10.1155/2021/6748920

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…Jang et al [17] identify the impact of input variables by using the long short-term memory recurrent neural network to predict the probability of bankruptcy for the US construction market. Wang [18] uses the combination of an ant colony algorithm and a neural network algorithm to build an early warning system for financial management. Park and Shin [19] employ a combination of random forest algorithms and the Bayesian regulatory neural network to monitor the financial solvency of companies for Korean insurers.…”

Section: Literature Reviewmentioning

confidence: 99%

Security Warning Model of Chinese Corporate Bond Defaults Based on Deep Neural Networks

Zhang

Chen

et al. 2022

Security and Communication Networks

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The Chinese bond market has achieved rapid development over the years. However, since the “rigid payment” in China was broken in 2014, the number and size of bond defaults have climbed up promptly and caused huge volatility in bond and even stock markets. To better manage and control the risk of the Chinese corporate bonds market, deep learning can be used as a helpful tool to predict the corporate default risk. This paper constructs a security warning model based on deep neural networks after a reasonable selection of characteristic indicators. By comparing Multi-Layer Perceptron (MLP) with the logistic regression method, it is found that MLP is more suitable in the security warning model to predict the default risk. And, hyper-parameter analyses and ablation study are conducted to explore the performance and accuracy of the best MLP settings. The experimental results show that the deep learning method accommodating the widely chosen factors in the security warning model is effective in predicting corporate bond defaults in China.

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Section: Literature Reviewmentioning

confidence: 99%

Security Warning Model of Chinese Corporate Bond Defaults Based on Deep Neural Networks

Zhang

Chen

et al. 2022

Security and Communication Networks

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“…As we all know, sufficient data are the premise of superior performances of these artificial intelligence learning methods; especially, deep learning models are developed to train massive data initially [ 18 , 19 ]. But real-world text data often follow a long-tailed distribution as the frequency of each class is typically different, such as news topic classification, clinical name entities recognition, and disease diagnosis for electronic medical records [ 20 – 22 ]. It means that a dataset can have a large number of under-represented classes (tail classes) and a few classes with more than sufficient data (head classes).…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Feature Space Reconstruction with Three-Way Decisions for Long-Tailed Text Classification

et al. 2022

Computational Intelligence and Neuroscience

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Text classification is widely studied by researchers in the natural language processing field. However, real-world text data often follow a long-tailed distribution as the frequency of each class is typically different. The performance of current mainstream learning algorithms in text classification suffers when the training data are highly imbalanced. The problem can get worse when the categories with fewer data are severely undersampled to the extent that the variation within each category is not fully captured by the given data. At present, there are a few studies on long-tailed text classification which put forward effective solutions. Encouraged by the progress of handling long-tailed data in the field of image, we try to integrate effective ideas into the field of long-tailed text classification and prove the effectiveness. In this paper, we come up with a novel approach of feature space reconstruction with the help of three-way decisions (3WDs) for long-tailed text classification. In detail, we verify the rationality of using a 3WD model for feature selection in long-tailed text data classification, propose a new feature space reconstruction method for long-tailed text data for the first time, and demonstrate how to effectively generate new samples for tail classes in reconstructed feature space. By adding new samples, we enrich the representing information of tail classes, to improve the classification results of long-tailed text classification. After some comparative experiments, we have verified that our model is an effective strategy to improve the performance of long-tailed text classification.

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“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

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confidence: 99%

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

mentioning

confidence: 99%