Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble
With new sensor systems that capture sky survey at high quality level, analyzing the resulting data within a limited time frame appears to be the next challenge. Specific to the GOTO project, this task proves to be crucial to discover new transients from a pool of large candidates. Initial works bas...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-09-01
|
| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S131915782100166X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849315850254811136 |
|---|---|
| author | Tossapon Boongoen Natthakan Iam-On James Mullaney |
| author_facet | Tossapon Boongoen Natthakan Iam-On James Mullaney |
| author_sort | Tossapon Boongoen |
| collection | DOAJ |
| description | With new sensor systems that capture sky survey at high quality level, analyzing the resulting data within a limited time frame appears to be the next challenge. Specific to the GOTO project, this task proves to be crucial to discover new transients from a pool of large candidates. Initial works based on the feature-based approach design this detection as imbalance classification, where a data-level method can be used to resolve the difference in cardinality between classes. This paper presents a context generation framework to complement the previously proposed model. In particular, samples are clustered to form data contexts to which different learning strategies may be applied. To ensure the quality of data clustering, a noise-induced cluster ensemble technique that has been recently introduced in the literature is employed here. The results with simulated data and algorithms of NB, C4.5 and KNN have shown that the proposed framework can filter out some negative samples quickly, while making classification of the rest more effective. In particular, it enhances predictive performance of basic classifiers by lifting F1 scores from less than 0.1 to around 0.3–0.5. Besides, parameter analysis is also given as a guideline for its application. |
| format | Article |
| id | doaj-art-a34a31d154644075b1f66820c682cab6 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-a34a31d154644075b1f66820c682cab62025-08-20T03:52:02ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-09-013485007501910.1016/j.jksuci.2021.06.019Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensembleTossapon Boongoen0Natthakan Iam-On1James Mullaney2Center of Excellence in AI and Emerging Technologies, School of Information Technology, Mae Fah Luang University, Tasud, Muang District, Chiang Rai 57100, ThailandCenter of Excellence in AI and Emerging Technologies, School of Information Technology, Mae Fah Luang University, Tasud, Muang District, Chiang Rai 57100, Thailand; Corresponding author.Department of Physics and Astronomy, University of Sheffield, UKWith new sensor systems that capture sky survey at high quality level, analyzing the resulting data within a limited time frame appears to be the next challenge. Specific to the GOTO project, this task proves to be crucial to discover new transients from a pool of large candidates. Initial works based on the feature-based approach design this detection as imbalance classification, where a data-level method can be used to resolve the difference in cardinality between classes. This paper presents a context generation framework to complement the previously proposed model. In particular, samples are clustered to form data contexts to which different learning strategies may be applied. To ensure the quality of data clustering, a noise-induced cluster ensemble technique that has been recently introduced in the literature is employed here. The results with simulated data and algorithms of NB, C4.5 and KNN have shown that the proposed framework can filter out some negative samples quickly, while making classification of the rest more effective. In particular, it enhances predictive performance of basic classifiers by lifting F1 scores from less than 0.1 to around 0.3–0.5. Besides, parameter analysis is also given as a guideline for its application.http://www.sciencedirect.com/science/article/pii/S131915782100166XAstronomical dataAnalytical methodMachine learningImbalance classificationCluster ensemble |
| spellingShingle | Tossapon Boongoen Natthakan Iam-On James Mullaney Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble Journal of King Saud University: Computer and Information Sciences Astronomical data Analytical method Machine learning Imbalance classification Cluster ensemble |
| title | Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble |
| title_full | Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble |
| title_fullStr | Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble |
| title_full_unstemmed | Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble |
| title_short | Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble |
| title_sort | providing contexts for classification of transients in a wide area sky survey an application of noise induced cluster ensemble |
| topic | Astronomical data Analytical method Machine learning Imbalance classification Cluster ensemble |
| url | http://www.sciencedirect.com/science/article/pii/S131915782100166X |
| work_keys_str_mv | AT tossaponboongoen providingcontextsforclassificationoftransientsinawideareaskysurveyanapplicationofnoiseinducedclusterensemble AT natthakaniamon providingcontextsforclassificationoftransientsinawideareaskysurveyanapplicationofnoiseinducedclusterensemble AT jamesmullaney providingcontextsforclassificationoftransientsinawideareaskysurveyanapplicationofnoiseinducedclusterensemble |