DF classification algorithm for constructing a small sample size of data-oriented DF regression model

Xia, Heng; Qiao, Junfei; Zhang, Jian; Yu, Wen

doi:10.1007/s00521-021-06809-7

Cited by 15 publications

(3 citation statements)

References 104 publications

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“…Its core mechanism is to take the needs of customers as the starting point, provide corresponding products and services, and establish marketing channels with their own characteristics, so as to form their own products and advantages [19]. Xia et al [20] pointed out that the overall quality of insurance marketing personnel is not high and the service level of the industry is low, which has seriously damaged the reputation of the insurance industry. Due to the lack of professional ethics or insurance and related knowledge of some employees, there are often violations such as misleading statements, premium rebates, and malicious solicitation when promoting insurance [20].…”

Section: Research Status At Home and Abroad Honka Andmentioning

confidence: 99%

“…Xia et al [20] pointed out that the overall quality of insurance marketing personnel is not high and the service level of the industry is low, which has seriously damaged the reputation of the insurance industry. Due to the lack of professional ethics or insurance and related knowledge of some employees, there are often violations such as misleading statements, premium rebates, and malicious solicitation when promoting insurance [20]. Karimi et al [21] proposed that under the circumstances of changing market demand and increasingly fierce market competition, insurance enterprises not only can meet the production and operation of existing products but must constantly develop new types of insurance to meet the needs of customers and market competition, as well as the need to expand market share [21].…”

Section: Research Status At Home and Abroad Honka Andmentioning

confidence: 99%

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Analysis of Insurance Marketing Planning Based on BD-Guided Decision Tree Classification Algorithm

Long

2022

Security and Communication Networks

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The emergence and development of Chinese insurance companies are affected by their own unique national conditions. The modern marketing concept lags behind, lacks the practical experience of scientifically formulating marketing strategies, and insurance practitioners lack marketing knowledge and the ability to absorb modern marketing achievements to guide practice. Therefore, China’s insurance industry inevitably has many problems in insurance marketing. In recent years, with the rapid development of big data (BD) technology, artificial intelligence, and machine learning in engineering and academia, relevant data models have been well developed. The advantages of the decision tree are its good robustness, full sample mining, high precision, fast implementation, fast running speed, and low implementation cost. This paper studies the application of the decision tree classification algorithm under the guidance of BD in insurance marketing planning. The running results of the decision tree classification algorithm model show what factors will affect the accuracy and recall rate of customer churn decision-making. The predicted value and scoring value of users are extracted to test the model, and the results are within a reasonable range. The running time of this model is 2,320.36 s, which is more efficient than the 34 min 25 s of traditional SAS. Therefore, the model can be put into use, and it is necessary to establish a long-term and stable relationship with customers.

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Section: Research Status At Home and Abroad Honka Andmentioning

confidence: 99%

Section: Research Status At Home and Abroad Honka Andmentioning

confidence: 99%

Analysis of Insurance Marketing Planning Based on BD-Guided Decision Tree Classification Algorithm

Long

2022

Security and Communication Networks

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“…Additionally, Nie et al [36] proposed an online multi-view deep forest architecture for remote sensing image data. DFCs offer advantages over DNNs, such as fewer hyperparameters, interpretability, and automatic adjustment of model complexity [37]. Moreover, they perform well with smaller image data samples, effectively resolving challenges in constructing DNN recognition models.…”

Section: Introductionmentioning

confidence: 99%

Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

Pan,

Tang,

Xia

et al. 2023

Sustainability

Self Cite

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The prevailing method for handling municipal solid waste (MSW) is incineration, a critical process that demands safe, stable, and eco-conscious operation. While grate-typed furnaces offer operational flexibility, they often generate pollution during unstable operating conditions. Moreover, fluctuations in the physical and chemical characteristics of MSW contribute to variable combustion statuses, accelerating internal furnace wear and ash accumulation. Tackling the challenges of pollution, wear, and efficiency in the MSW incineration (MSWI) process necessitates the automatic online recognition of combustion status. This article introduces a novel online recognition method using deep forest classification (DFC) based on convolutional multi-layer feature fusion. The method entails several key steps: initial collection and analysis of flame image modeling data and construction of an offline model utilizing LeNet-5 and DFC. Here, LeNet-5 trains to extract deep features from flame images, while an adaptive selection fusion method on multi-layer features selects the most effective fused deep features. Subsequently, these fused deep features feed into DFC, constructing an offline recognition model for identifying combustion status. Finally, embedding this recognition system into an existing MSWI process data monitoring system enables online flame video recognition. Experimental results show remarkable accuracies: 93.80% and 95.08% for left and right grate furnace offline samples, respectively. When implemented in an online flame video recognition platform, it aptly meets recognition demands.

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Virtual sample generation method based on generative adversarial fuzzy neural network

Cui

Xia

Qiao

et al. 2022

Neural Comput & Applic

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DF classification algorithm for constructing a small sample size of data-oriented DF regression model

Cited by 15 publications

References 104 publications

Analysis of Insurance Marketing Planning Based on BD-Guided Decision Tree Classification Algorithm

Analysis of Insurance Marketing Planning Based on BD-Guided Decision Tree Classification Algorithm

Online Combustion Status Recognition of Municipal Solid Waste Incineration Process Using DFC Based on Convolutional Multi-Layer Feature Fusion

Virtual sample generation method based on generative adversarial fuzzy neural network

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