Projects per year
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.
|Number of pages||13|
|Journal||Journal of King Saud University - Computer and Information Sciences|
|Early online date||09 Aug 2022|
|Publication status||Published - 01 Sept 2022|
- Analytical method
- Astronomical data
- Cluster ensemble
- Imbalance classification
- Machine learning
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- 1 Finished
Robust burnt scar profiling using deep learning and ensemble modelling with Remote sensing data
17 Feb 2021 → 16 Feb 2022
Project: Externally funded research