Feature selection for financial data classification: Islamic finance application
The rapid growth of computing technology to process vast amount of data has impelled more interest in data mining. Such interest was mainly aimed at knowledge discovery to improve decision making process in diverse range of applications, including Islamic finance. One of the most critical steps in...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/71851/ http://irep.iium.edu.my/71851/1/71851_Feature%20Selection%20for%20Financial%20Data%20Classification.pdf http://irep.iium.edu.my/71851/7/71851%20Feature%20selection%20for%20financial%20data%20classification%20SCOPUS.pdf |
| _version_ | 1848787520331972608 |
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| author | Kartiwi, Mira Gunawan, Teddy Surya Arundina, Tika Omar, Mohd. Azmi |
| author_facet | Kartiwi, Mira Gunawan, Teddy Surya Arundina, Tika Omar, Mohd. Azmi |
| author_sort | Kartiwi, Mira |
| building | IIUM Repository |
| collection | Online Access |
| description | The rapid growth of computing technology to
process vast amount of data has impelled more interest in data mining. Such interest was mainly aimed at knowledge discovery to improve decision making process in diverse range of applications, including Islamic finance. One of the most critical steps in data mining is data preprocessing, as it would directly affect the quality
of insights obtained at the later stage. Feature selection has been widely used in data preprocessing phase to improve the machine learning algorithm and model interpretability. However, there has been limited attention has been given on the evaluation of feature
selection methods on its effectiveness to process input data for Induction Decision Tree (IDT). Hence, this study aims to address such gap in the literature through the use of real-world data in Islamic finance to evaluate the improvement that generated by feature selection method. The result of the study shows that the use of such technique has resulted in better performance of the IDT
model generated in the study. |
| first_indexed | 2025-11-14T17:26:14Z |
| format | Proceeding Paper |
| id | iium-71851 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:26:14Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-718512019-06-11T07:52:02Z http://irep.iium.edu.my/71851/ Feature selection for financial data classification: Islamic finance application Kartiwi, Mira Gunawan, Teddy Surya Arundina, Tika Omar, Mohd. Azmi HG3368 Islamic Banking and Finance T58.6 Management information systems The rapid growth of computing technology to process vast amount of data has impelled more interest in data mining. Such interest was mainly aimed at knowledge discovery to improve decision making process in diverse range of applications, including Islamic finance. One of the most critical steps in data mining is data preprocessing, as it would directly affect the quality of insights obtained at the later stage. Feature selection has been widely used in data preprocessing phase to improve the machine learning algorithm and model interpretability. However, there has been limited attention has been given on the evaluation of feature selection methods on its effectiveness to process input data for Induction Decision Tree (IDT). Hence, this study aims to address such gap in the literature through the use of real-world data in Islamic finance to evaluate the improvement that generated by feature selection method. The result of the study shows that the use of such technique has resulted in better performance of the IDT model generated in the study. Institute of Electrical and Electronics Engineers Inc. 2019-04-15 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/71851/1/71851_Feature%20Selection%20for%20Financial%20Data%20Classification.pdf application/pdf en http://irep.iium.edu.my/71851/7/71851%20Feature%20selection%20for%20financial%20data%20classification%20SCOPUS.pdf Kartiwi, Mira and Gunawan, Teddy Surya and Arundina, Tika and Omar, Mohd. Azmi (2019) Feature selection for financial data classification: Islamic finance application. In: 2018 IEEE 5th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), 28th-30th November 2018, Songkla, Thailand. https://ieeexplore.ieee.org/document/8688803 10.1109/ICSIMA.2018.8688803 |
| spellingShingle | HG3368 Islamic Banking and Finance T58.6 Management information systems Kartiwi, Mira Gunawan, Teddy Surya Arundina, Tika Omar, Mohd. Azmi Feature selection for financial data classification: Islamic finance application |
| title | Feature selection for financial data classification:
Islamic finance application |
| title_full | Feature selection for financial data classification:
Islamic finance application |
| title_fullStr | Feature selection for financial data classification:
Islamic finance application |
| title_full_unstemmed | Feature selection for financial data classification:
Islamic finance application |
| title_short | Feature selection for financial data classification:
Islamic finance application |
| title_sort | feature selection for financial data classification:
islamic finance application |
| topic | HG3368 Islamic Banking and Finance T58.6 Management information systems |
| url | http://irep.iium.edu.my/71851/ http://irep.iium.edu.my/71851/ http://irep.iium.edu.my/71851/ http://irep.iium.edu.my/71851/1/71851_Feature%20Selection%20for%20Financial%20Data%20Classification.pdf http://irep.iium.edu.my/71851/7/71851%20Feature%20selection%20for%20financial%20data%20classification%20SCOPUS.pdf |