Summary of Missing Data Imputation with Granular Semantics and Ai-driven Pipeline For Bankruptcy Prediction, by Debarati Chakraborty and Ravi Ranjan
Missing Data Imputation With Granular Semantics and AI-driven Pipeline for Bankruptcy Prediction
by Debarati Chakraborty, Ravi Ranjan
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Statistical Finance (q-fin.ST); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a pipeline for predicting bankruptcy, addressing challenges like missing values, high-dimensional data, and class imbalance. A novel method for imputing missing data using granular semantics is introduced, which leverages feature semantics and reliable observations in a low-dimensional space to predict missing values. The granules are formed around each missing entry, considering correlated features and reliable observations to preserve relevance and reliability. An intergranular prediction is then performed for imputation within these contextual granules. This method overcomes the need to access the entire database repetitively for each missing value. The proposed pipeline is tested on the Polish Bankruptcy dataset, providing an efficient solution for big and high-dimensional datasets with large imputation rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a way to predict when a company might go bankrupt. They had trouble because some data was missing, there were many features that weren’t useful, and most of the companies in their test group didn’t actually go bankrupt (this is called class imbalance). To fix these problems, they developed a new method for filling in missing data that looks at patterns in the remaining data to make good predictions. They also used some clever techniques to reduce the number of features and balance out the classes so that all types of companies were equally represented. The results show that this pipeline is effective at predicting bankruptcy. |
Keywords
* Artificial intelligence * Semantics
