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Analyzing Instance Hardness in ML by FiveBase is a document available to read on EtoBox.
This paper investigates instance-level data complexity in machine learning, focusing on instances that are frequently misclassified by learning algorithms. By analyzing over 190,000 instances from 64 datasets, the authors identify that class overlap significantly contributes to instance hardness, which affects classification accuracy. The study proposes integrating instance hardness into the learning process to enhance algorithm performance and offers a set of hardness measures to understand the reasons beh
- Author
- FiveBase
- Language
- EN