abnormal data detection and learning their behavior by abnormality and satisficing theory
Learning of abnormalities is a considerable challenge in data mining and knowledge discovery. Exceptional phenomena detect among huge records of the database which contains a large number of normal records and very few abnormal ones. This is important to promote confidence to a limited number of rec...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Tehran
2015-12-01
|
| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_55639_f6b049d36513760c8ba1504e87c90630.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849324366238580736 |
|---|---|
| author | masood abessi Elahe Hajigol Yazdi |
| author_facet | masood abessi Elahe Hajigol Yazdi |
| author_sort | masood abessi |
| collection | DOAJ |
| description | Learning of abnormalities is a considerable challenge in data mining and knowledge discovery. Exceptional phenomena detect among huge records of the database which contains a large number of normal records and very few abnormal ones. This is important to promote confidence to a limited number of records for effective learning of abnormality. In this study, a new approach based on the abnormality theory and satisficing theory presented for confidence improvement of abnormal data detection and learning. First, the borders of abnormal and normal behavior clear using a combination approach based on abnormality theory then, satisfied solution extracted by means of satisficing theory. Modified RISE method as a bottom-up learning approach implemented to extract Normal and abnormal knowledge. The efficiency of the proposed model determined by using it, for abnormal stock selection from the Iran stock market. The superior of the proposed method results toward the results of applying decision tree and support vector machine is considerable. Accuracy of proposed method measure by g-means index. The results show the capability of proposed approach in abnormality detection and learning. |
| format | Article |
| id | doaj-art-92a21fd8c0bb477cbbfbc931c8d0d8a0 |
| institution | Kabale University |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2015-12-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-92a21fd8c0bb477cbbfbc931c8d0d8a02025-08-20T03:48:42ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592015-12-017482584410.22059/jitm.2015.5563955639abnormal data detection and learning their behavior by abnormality and satisficing theorymasood abessi0Elahe Hajigol Yazdi1استادیار مدیریت صنعتی، دانشکدۀ صنایع، دانشگاه یزد، یزد، ایراندانشجوی دکتری مهندسی صنایع، دانشکدۀ صنایع، دانشگاه یزد، یزد، ایرانLearning of abnormalities is a considerable challenge in data mining and knowledge discovery. Exceptional phenomena detect among huge records of the database which contains a large number of normal records and very few abnormal ones. This is important to promote confidence to a limited number of records for effective learning of abnormality. In this study, a new approach based on the abnormality theory and satisficing theory presented for confidence improvement of abnormal data detection and learning. First, the borders of abnormal and normal behavior clear using a combination approach based on abnormality theory then, satisfied solution extracted by means of satisficing theory. Modified RISE method as a bottom-up learning approach implemented to extract Normal and abnormal knowledge. The efficiency of the proposed model determined by using it, for abnormal stock selection from the Iran stock market. The superior of the proposed method results toward the results of applying decision tree and support vector machine is considerable. Accuracy of proposed method measure by g-means index. The results show the capability of proposed approach in abnormality detection and learning.https://jitm.ut.ac.ir/article_55639_f6b049d36513760c8ba1504e87c90630.pdfData MiningAbnormality theorySatisficing theoryBottom-up learning |
| spellingShingle | masood abessi Elahe Hajigol Yazdi abnormal data detection and learning their behavior by abnormality and satisficing theory Journal of Information Technology Management Data Mining Abnormality theory Satisficing theory Bottom-up learning |
| title | abnormal data detection and learning their behavior by abnormality and satisficing theory |
| title_full | abnormal data detection and learning their behavior by abnormality and satisficing theory |
| title_fullStr | abnormal data detection and learning their behavior by abnormality and satisficing theory |
| title_full_unstemmed | abnormal data detection and learning their behavior by abnormality and satisficing theory |
| title_short | abnormal data detection and learning their behavior by abnormality and satisficing theory |
| title_sort | abnormal data detection and learning their behavior by abnormality and satisficing theory |
| topic | Data Mining Abnormality theory Satisficing theory Bottom-up learning |
| url | https://jitm.ut.ac.ir/article_55639_f6b049d36513760c8ba1504e87c90630.pdf |
| work_keys_str_mv | AT masoodabessi abnormaldatadetectionandlearningtheirbehaviorbyabnormalityandsatisficingtheory AT elahehajigolyazdi abnormaldatadetectionandlearningtheirbehaviorbyabnormalityandsatisficingtheory |