A case-based reasoning driven ensemble learning paradigm for financial distress prediction with missing data

Yu, Lean; Li, Mengxin

doi:10.1016/j.asoc.2023.110163

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The Kida Model Application1

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2024

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Cited by 18 publications

(2 citation statements)

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“…The Kida Model, established in 1981, is a robust tool designed to identify potential financial failure and bankruptcy risks in companies [21].…”

Section: The Kida Model Applicationmentioning

confidence: 99%

Predicting Financial Failure in Algerian Public Insurance Companies Using the Kida Model

Mili

2024

J. Appl. Data Sci.

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“…The Kida Model, established in 1981, is a robust tool designed to identify potential financial failure and bankruptcy risks in companies [21].…”

Section: The Kida Model Applicationmentioning

confidence: 99%

Predicting Financial Failure in Algerian Public Insurance Companies Using the Kida Model

Mili

2024

J. Appl. Data Sci.

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“…The distribution pattern of multivariate time series data such as finance, medical treatment and weather is clear and the fluctuation range is large, which is easy to fill the missing value [10][11][12]. The parameters of industrial systems are relatively stable under normal operating conditions and fluctuate for a long time within a small range, which means that sensor data has a unique distribution [13].…”

Section: Introductionmentioning

confidence: 99%

Missing data filling in soft sensing using denoising diffusion probability model

Jiang,

Wang,

Shen

et al. 2023

Meas. Sci. Technol.

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With the aim of addressing the problem of degradation in soft measurement accuracy due to missing data in industrial processes, a filling method based on the denoising diffusion probability model (DDPM) is proposed here to improve the accuracy of soft measurement modeling. First, missing regions are detected with the help of an improved Isolation Forest algorithm to obtain information such as the locations and numbers of missing data regions. Next, a data generation model is constructed based on DDPM and new samples are obtained. By adjusting the threshold for normal operation of the system and the weight sampler, filler samples that are similar to the distribution of the original data can be filtered from the new samples to form a complete dataset. The feasibility of the proposed missing data filling method is explored through numerical simulations, and its superiority in terms of improving the prediction accuracy of soft measurements is verified in regard to the nickel flash smelting process.

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Ensemble Stacking Case-Based Reasoning for Regression

Soto-Forero,

Betbeder,

Henriet

2024

Lecture Notes in Computer Science

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A case-based reasoning driven ensemble learning paradigm for financial distress prediction with missing data

Cited by 18 publications

References 42 publications

Predicting Financial Failure in Algerian Public Insurance Companies Using the Kida Model

Predicting Financial Failure in Algerian Public Insurance Companies Using the Kida Model

Missing data filling in soft sensing using denoising diffusion probability model

Ensemble Stacking Case-Based Reasoning for Regression

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