Instance Selection: A Bayesian Decision Theory Perspective
DOI:
https://doi.org/10.1609/aaai.v36i6.20578Keywords:
Machine Learning (ML), Data Mining & Knowledge Management (DMKM)Abstract
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.Downloads
Published
2022-06-28
How to Cite
Chen, Q., Cao, F., Xing, Y., & Liang, J. (2022). Instance Selection: A Bayesian Decision Theory Perspective. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6287-6294. https://doi.org/10.1609/aaai.v36i6.20578
Issue
Section
AAAI Technical Track on Machine Learning I