Accuracy Analysis for Logistic Regression Algorithm and Random Forest Algorithm to Detect Frauds in Mobile Money Transaction

Kumar, G. Sudesh; Nalini, Dr.M.

doi:10.47059/revistageintec.v11i4.2182

Cited by 5 publications

(3 citation statements)

References 26 publications

(24 reference statements)

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“…Based on the above selected features of multi-channel collaborative fee urging, an intelligent decision-making model of multi-channel collaborative fee urging is constructed by using the Logistic regression algorithm [10].…”

Section: Build An Intelligent Decision-making Model For Multi-channel...mentioning

confidence: 99%

Construction of intelligent decision model for multi-channel collaborative fee urging

Shangguan¹,

Cai²,

Ke³

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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In order to effectively replace the daily fee urging work of the grid manager, realize the lean fee urging ability, give full play to the channel efficiency, and improve the efficiency of multi-channel collaborative fee urging and customer experience, an intelligent decision-making model for multi-channel collaborative fee urging was built. The sliding window and the anti k-nearest neighbor method are used to obtain multi-channel collaborative fee urging samples. According to the availability, integrity and business relevance of the data, based on incremental principal component analysis, combined with entropy method, the feature of multi-channel collaborative fee urging is selected. By using Logistic regression algorithm, an intelligent decision model of multi-channel collaborative fee urging is built to realize intelligent decision oriented to multi-channel collaborative fee urging. The experimental results show that the proposed method has a strong multi-channel collaborative fee urging capability and can effectively improve the efficiency of multi-channel collaborative fee urging.

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Section: Build An Intelligent Decision-making Model For Multi-channel...mentioning

confidence: 99%

Construction of intelligent decision model for multi-channel collaborative fee urging

Shangguan¹,

Cai²,

Ke³

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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“…Based on the above preprocessed annual contribution power prediction characteristic data, and based on Logistic regression analysis [10], the annual contribution power prediction model is built. The main calculation formula is as follows:…”

Section: Build Annual Contribution Electricity Forecast Modelmentioning

confidence: 99%

“…𝜅 𝑠 (10) In Formula (10), 𝜅 𝑑 is the operating profit of the current period and 𝜅 𝑠 is the operating profit of the previous period.…”

Section: Build Annual Contribution Electricity Forecast Modelmentioning

confidence: 99%

Annual Contribution Electricity Forecasting Model Based on Logistic Regression Analysis

Lai¹,

Wu²,

Lan³

et al. 2023

J. Phys.: Conf. Ser.

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Due to the complex characteristics of the annual contribution time series, it is difficult to achieve the ideal prediction effect by a single prediction. Therefore, the annual contribution electricity prediction model based on Logistic regression analysis is studied. The statistical method of time series analysis combined with the fuzzy correlation feature analysis method is used to obtain the high-voltage power transmission data of business expansion and the power consumption data after power transmission. We use the fuzzy clustering theory to complete the customer segmentation and accurately locate the same type of user groups. On this basis, we preprocess the characteristic data of the annual contribution electricity forecast and build the annual contribution electricity forecast model based on Logistic regression analysis to realize the annual contribution electricity forecast. The experimental results show that the proposed method has a good prediction effect of annual contribution power, and can effectively shorten the prediction time of annual contribution power.

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Classification of pigmented lesion using novel k-nearest neighbor comparing with logistic regression algorithm for better accuracy

Sha,

Saravanan

2024

Fifth International Conference on Applied Sciences: Icas2023

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Accuracy Analysis for Logistic Regression Algorithm and Random Forest Algorithm to Detect Frauds in Mobile Money Transaction

Cited by 5 publications

References 26 publications

Construction of intelligent decision model for multi-channel collaborative fee urging

Construction of intelligent decision model for multi-channel collaborative fee urging

Annual Contribution Electricity Forecasting Model Based on Logistic Regression Analysis

Classification of pigmented lesion using novel k-nearest neighbor comparing with logistic regression algorithm for better accuracy

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