An Enhanced Bankruptcy Prediction Model Using Fuzzy Clustering Model and Random Forest Algorithm
Bibliographic record
Abstract
The rapid advancements made in Information Technologies (IT) have evolved in the prediction model for financial data. Research in the prediction of bankruptcy is inclining to owe to the growth of related associations with economic and social phenomena. Financial crises in recent scenarios have influenced the growth of financial institutions. Hence, the need for bankruptcy risk prediction at an earlier stage is of prime importance. Though several prediction algorithms were suggested, the predictive models' accuracy is still a challenging task. In this paper, a bankruptcy prediction model is developed by integrating the Fuzzy clustering model and Multi-objective random forest classifiers. The voluminous number of records of the financial dataset and polish bankruptcy dataset is collected from a public repository. It is pre-processed using a MapReduce technique, one of the Big Data approaches. Benefits given by big data approaches help to achieve better flexibility towards a variable declaration. The collected records are pre-processed and organized under efficient index construction. FCM is employed to cluster the data for analytic purposes. Finally, a multi-objective Random Forest classifier helps to develop a prediction model for bankruptcy. Experimental analysis is carried out with accuracy, precision, sensitivity, and specificity compared with existing, Genetic algorithms. Compared to the existing technique, the proposed technique has obtained 80% accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".