The Usefulness of Feature Selection in Auditors Opinion Type Prediction
Abstract: Despite the importance of predictive variable in prediction, in most of the research in the field of auditors’ opinion the purpose was rendering the suitable models. Meanwhile, less attention was paid to the selection of optimal predictive variable and appropriate models of these selection...
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| Format: | Article |
| Language: | fas |
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University of Tehran
2016-11-01
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| Series: | بررسی‌های حسابداری و حسابرسی |
| Subjects: | |
| Online Access: | https://acctgrev.ut.ac.ir/article_59781_dc03b5f6dbfcea746e1a38763a95fbd1.pdf |
| _version_ | 1818789260921143296 |
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| author | Mohammad Setayesh Mostafa kazem Nejad Gholam Reza Rezaei Ali asghar Dehghani |
| author_facet | Mohammad Setayesh Mostafa kazem Nejad Gholam Reza Rezaei Ali asghar Dehghani |
| author_sort | Mohammad Setayesh |
| collection | DOAJ |
| description | Abstract: Despite the importance of predictive variable in prediction, in most of the research in the field of auditors’ opinion the purpose was rendering the suitable models. Meanwhile, less attention was paid to the selection of optimal predictive variable and appropriate models of these selection. Therefore, in most of these research the predictive variables were chosen randomly and according to the prior research. The process of selecting variables could be used as a preprocess for omitting irrelevant variables and selecting optimal variables before creating the model. In this regard, this study investigates the usefulness of Correlation-Based Features Selection (CFS) in auditors’ opinion prediction of listed companies in Tehran Stock Exchange. The classifiers including Artificial Neural Networks (ANN) and logistic regression were used. In overall, the experimental results of investigating 1214 firms-years from 2008 to 2015, confirmed the usefulness of CFS Method in predicting auditors' opinion. In other words, the application of the CFS method, increases the mean of accuracy in comparison with using all variables, and reduces the occurrence of type I and type II errors. Furthermore, the results indicated that ANN outperforms the logistic regression. |
| first_indexed | 2024-12-18T14:36:45Z |
| format | Article |
| id | doaj.art-6047fe6f4722410d8b2250e9535b56c8 |
| institution | Directory Open Access Journal |
| issn | 2645-8020 2645-8039 |
| language | fas |
| last_indexed | 2024-12-18T14:36:45Z |
| publishDate | 2016-11-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | بررسی‌های حسابداری و حسابرسی |
| spelling | doaj.art-6047fe6f4722410d8b2250e9535b56c82022-12-21T21:04:30ZfasUniversity of Tehranبررسی‌های حسابداری و حسابرسی2645-80202645-80392016-11-0123337339210.22059/acctgrev.2016.5978159781The Usefulness of Feature Selection in Auditors Opinion Type PredictionMohammad Setayesh0Mostafa kazem Nejad1Gholam Reza Rezaei2Ali asghar Dehghani3استاد حسابداری، دانشگاه شیراز، شیراز، ایراندکتری حسابداری، دانشگاه شیراز، شیراز، ایراندانشجوی دکتری حسابداری، دانشگاه شیراز، شیراز، ایرانکارشناسارشد حسابداری، دانشگاه شیراز، شیراز، ایرانAbstract: Despite the importance of predictive variable in prediction, in most of the research in the field of auditors’ opinion the purpose was rendering the suitable models. Meanwhile, less attention was paid to the selection of optimal predictive variable and appropriate models of these selection. Therefore, in most of these research the predictive variables were chosen randomly and according to the prior research. The process of selecting variables could be used as a preprocess for omitting irrelevant variables and selecting optimal variables before creating the model. In this regard, this study investigates the usefulness of Correlation-Based Features Selection (CFS) in auditors’ opinion prediction of listed companies in Tehran Stock Exchange. The classifiers including Artificial Neural Networks (ANN) and logistic regression were used. In overall, the experimental results of investigating 1214 firms-years from 2008 to 2015, confirmed the usefulness of CFS Method in predicting auditors' opinion. In other words, the application of the CFS method, increases the mean of accuracy in comparison with using all variables, and reduces the occurrence of type I and type II errors. Furthermore, the results indicated that ANN outperforms the logistic regression.https://acctgrev.ut.ac.ir/article_59781_dc03b5f6dbfcea746e1a38763a95fbd1.pdfCorrelation-Based Features Selection (CFS) methodAuditors Opinion TypeArtificial Neural NetworksLogistic Regression |
| spellingShingle | Mohammad Setayesh Mostafa kazem Nejad Gholam Reza Rezaei Ali asghar Dehghani The Usefulness of Feature Selection in Auditors Opinion Type Prediction بررسی‌های حسابداری و حسابرسی Correlation-Based Features Selection (CFS) method Auditors Opinion Type Artificial Neural Networks Logistic Regression |
| title | The Usefulness of Feature Selection in Auditors Opinion Type Prediction |
| title_full | The Usefulness of Feature Selection in Auditors Opinion Type Prediction |
| title_fullStr | The Usefulness of Feature Selection in Auditors Opinion Type Prediction |
| title_full_unstemmed | The Usefulness of Feature Selection in Auditors Opinion Type Prediction |
| title_short | The Usefulness of Feature Selection in Auditors Opinion Type Prediction |
| title_sort | usefulness of feature selection in auditors opinion type prediction |
| topic | Correlation-Based Features Selection (CFS) method Auditors Opinion Type Artificial Neural Networks Logistic Regression |
| url | https://acctgrev.ut.ac.ir/article_59781_dc03b5f6dbfcea746e1a38763a95fbd1.pdf |
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