A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for Classification
Classification is one of the main areas of machine learning, where the target variable is usually categorical with at least two levels. This study focuses on deducing an optimal cut-off point for continuous outcomes (e.g., predicted probabilities) resulting from binary classifiers. To achieve this a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2024/8082372 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850213813940387840 |
|---|---|
| author | O.-A. Ampomah R. Minkah G. Kallah-Dagadu E. N. N. Nortey |
| author_facet | O.-A. Ampomah R. Minkah G. Kallah-Dagadu E. N. N. Nortey |
| author_sort | O.-A. Ampomah |
| collection | DOAJ |
| description | Classification is one of the main areas of machine learning, where the target variable is usually categorical with at least two levels. This study focuses on deducing an optimal cut-off point for continuous outcomes (e.g., predicted probabilities) resulting from binary classifiers. To achieve this aim, the study modified univariate discriminant functions by incorporating the error cost of misclassification penalties involved. By doing so, we can systematically shift the cut-off point within its measurement range till the optimal point is obtained. Extensive simulation studies were conducted to investigate the performance of the proposed method in comparison with existing classification methods under the binary logistic and Bayesian quantile regression frameworks. The simulation results indicate that logistic regression models incorporating the proposed method outperform the existing ordinary logistic regression and Bayesian regression models. We illustrate the proposed method with a practical dataset from the finance industry that assesses default status in home equity. |
| format | Article |
| id | doaj-art-877bde2d650d47ef82e86d85f3c54de6 |
| institution | OA Journals |
| issn | 1687-9538 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-877bde2d650d47ef82e86d85f3c54de62025-08-20T02:09:04ZengWileyJournal of Probability and Statistics1687-95382024-01-01202410.1155/2024/8082372A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for ClassificationO.-A. Ampomah0R. Minkah1G. Kallah-Dagadu2E. N. N. Nortey3Department of Statistics and Actuarial ScienceDepartment of Statistics and Actuarial ScienceDepartment of Statistics and Actuarial ScienceDepartment of Statistics and Actuarial ScienceClassification is one of the main areas of machine learning, where the target variable is usually categorical with at least two levels. This study focuses on deducing an optimal cut-off point for continuous outcomes (e.g., predicted probabilities) resulting from binary classifiers. To achieve this aim, the study modified univariate discriminant functions by incorporating the error cost of misclassification penalties involved. By doing so, we can systematically shift the cut-off point within its measurement range till the optimal point is obtained. Extensive simulation studies were conducted to investigate the performance of the proposed method in comparison with existing classification methods under the binary logistic and Bayesian quantile regression frameworks. The simulation results indicate that logistic regression models incorporating the proposed method outperform the existing ordinary logistic regression and Bayesian regression models. We illustrate the proposed method with a practical dataset from the finance industry that assesses default status in home equity.http://dx.doi.org/10.1155/2024/8082372 |
| spellingShingle | O.-A. Ampomah R. Minkah G. Kallah-Dagadu E. N. N. Nortey A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for Classification Journal of Probability and Statistics |
| title | A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for Classification |
| title_full | A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for Classification |
| title_fullStr | A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for Classification |
| title_full_unstemmed | A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for Classification |
| title_short | A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for Classification |
| title_sort | cost of misclassification adjustment approach for estimating optimal cut off point for classification |
| url | http://dx.doi.org/10.1155/2024/8082372 |
| work_keys_str_mv | AT oaampomah acostofmisclassificationadjustmentapproachforestimatingoptimalcutoffpointforclassification AT rminkah acostofmisclassificationadjustmentapproachforestimatingoptimalcutoffpointforclassification AT gkallahdagadu acostofmisclassificationadjustmentapproachforestimatingoptimalcutoffpointforclassification AT ennnortey acostofmisclassificationadjustmentapproachforestimatingoptimalcutoffpointforclassification AT oaampomah costofmisclassificationadjustmentapproachforestimatingoptimalcutoffpointforclassification AT rminkah costofmisclassificationadjustmentapproachforestimatingoptimalcutoffpointforclassification AT gkallahdagadu costofmisclassificationadjustmentapproachforestimatingoptimalcutoffpointforclassification AT ennnortey costofmisclassificationadjustmentapproachforestimatingoptimalcutoffpointforclassification |