Item Infomation
Title: |
Hostility measure for multi-level study of data complexity |
Authors: |
Carmen, Lancho Isaac Martín De Diego, Diego Marina, Cuesta |
Issue Date: |
2023 |
Publisher: |
Springer |
Abstract: |
omplexity measures aim to characterize the underlying complexity of supervised data. These measures tackle factors hindering the performance of Machine Learning (ML) classifiers like overlap, density, linearity, etc. The state-of-the-art has mainly focused on the dataset perspective of complexity, i.e., offering an estimation of the complexity of the whole dataset. Recently, the instance perspective has also been addressed. In this paper, the hostility measure, a complexity measure offering a multi-level (instance, class, and dataset) perspective of data complexity is proposed. |
Description: |
CC BY |
URI: |
https://link.springer.com/article/10.1007/s10489-022-03793-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/7392 |
Appears in Collections |
OER - Công nghệ thông tin |
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