Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification
Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) techn...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ital Publication
2025-03-01
|
| Series: | HighTech and Innovation Journal |
| Subjects: | |
| Online Access: | https://hightechjournal.org/index.php/HIJ/article/view/1114 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850273996161941504 |
|---|---|
| author | Anastasya Latubessy Retantyo Wardoyo Aina Musdholifah Sri Kusrohmaniah |
| author_facet | Anastasya Latubessy Retantyo Wardoyo Aina Musdholifah Sri Kusrohmaniah |
| author_sort | Anastasya Latubessy |
| collection | DOAJ |
| description | Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used.
Doi: 10.28991/HIJ-2025-06-01-05
Full Text: PDF |
| format | Article |
| id | doaj-art-d995835a0bdd4563a9b9ab47d40cf279 |
| institution | OA Journals |
| issn | 2723-9535 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | HighTech and Innovation Journal |
| spelling | doaj-art-d995835a0bdd4563a9b9ab47d40cf2792025-08-20T01:51:16ZengItal PublicationHighTech and Innovation Journal2723-95352025-03-0161678010.28991/HIJ-2025-06-01-05241Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder ClassificationAnastasya Latubessy0Retantyo Wardoyo1Aina Musdholifah2Sri Kusrohmaniah31) Doctoral Program Department of Computer Science and Electronics, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia. 2) Department of Informatics Engineering, Faculty of Engineering, Universitas Muria Kudus, Indonesia.Department of Computer Science and Electronics, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Yogyakarta 55281,Department of Computer Science and Electronics, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Yogyakarta 55281,Department of Psychology, Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta 55281,Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. Doi: 10.28991/HIJ-2025-06-01-05 Full Text: PDFhttps://hightechjournal.org/index.php/HIJ/article/view/1114classificationattribute weightedfine-tunenaïve bayesinstances reductiongaming disorder. |
| spellingShingle | Anastasya Latubessy Retantyo Wardoyo Aina Musdholifah Sri Kusrohmaniah Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification HighTech and Innovation Journal classification attribute weighted fine-tune naïve bayes instances reduction gaming disorder. |
| title | Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification |
| title_full | Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification |
| title_fullStr | Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification |
| title_full_unstemmed | Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification |
| title_short | Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification |
| title_sort | fine tuned attribute weighted naive bayes with modified partial instances reduction for gaming disorder classification |
| topic | classification attribute weighted fine-tune naïve bayes instances reduction gaming disorder. |
| url | https://hightechjournal.org/index.php/HIJ/article/view/1114 |
| work_keys_str_mv | AT anastasyalatubessy finetunedattributeweightednaivebayeswithmodifiedpartialinstancesreductionforgamingdisorderclassification AT retantyowardoyo finetunedattributeweightednaivebayeswithmodifiedpartialinstancesreductionforgamingdisorderclassification AT ainamusdholifah finetunedattributeweightednaivebayeswithmodifiedpartialinstancesreductionforgamingdisorderclassification AT srikusrohmaniah finetunedattributeweightednaivebayeswithmodifiedpartialinstancesreductionforgamingdisorderclassification |