Instance Selection: A Bayesian Decision Theory Perspective
| العنوان: | Instance Selection: A Bayesian Decision Theory Perspective |
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| المؤلفون: | Qingqiang Chen, Fuyuan Cao, Ying Xing, Jiye Liang |
| المصدر: | Proceedings of the AAAI Conference on Artificial Intelligence. 36:6287-6294 |
| بيانات النشر: | Association for the Advancement of Artificial Intelligence (AAAI), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | General Medicine |
| الوصف: | In this paper, we consider the problem of lacking theoretical foundation and low execution efficiency of the instance selection methods based on the k-nearest neighbour rule when processing large-scale data. We point out that the core idea of these methods can be explained from the perspective of Bayesian decision theory, that is, to find which instances are reducible, irreducible, and deleterious. Then, based on the percolation theory, we establish the relationship between these three types of instances and local homogeneous cluster (i.e., a set of instances with the same labels). Finally, we propose a method based on an accelerated k-means algorithm to construct local homogeneous clusters and remove the superfluous instances. The performance of our method is studied on extensive synthetic and benchmark data sets. Our proposed method can handle large-scale data more effectively than the state-of-the-art instance selection methods. All code and data results are available at https://github.com/CQQXY161120/Instance-Selection. |
| تدمد: | 2374-3468 2159-5399 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::f3abb7cb5edea7dc8dde22d302c52ea0 https://doi.org/10.1609/aaai.v36i6.20578 |
| رقم الأكسشن: | edsair.doi...........f3abb7cb5edea7dc8dde22d302c52ea0 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23743468 21595399 |
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