Summary of Parallel Algorithm For Optimal Threshold Labeling Of Ordinal Regression Methods, by Ryoya Yamasaki and Toshiyuki Tanaka
Parallel Algorithm for Optimal Threshold Labeling of Ordinal Regression Methods
by Ryoya Yamasaki, Toshiyuki Tanaka
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new algorithm for ordinal regression (OR) tasks, which classify ordinal data into one of K classes. The algorithm learns a one-dimensional transformation (1DT) of the explanatory variable that preserves the order of label values, and then assigns a label prediction based on the rank of an interval. The proposed algorithm is parallelizable, reducing computation time by approximately 60% compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research develops a new way to classify data that has a natural order or ranking. The method uses a special transformation to keep the order correct and then assigns a label based on where something falls in relation to other things. This new algorithm is faster than previous methods because it can be processed in parallel. |
Keywords
» Artificial intelligence » Regression
