Summary of Constructing Variables Using Classifiers As An Aid to Regression: An Empirical Assessment, by Colin Troisemaine et al.
Constructing Variables Using Classifiers as an Aid to Regression: An Empirical Assessment
by Colin Troisemaine, Vincent Lemaire
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method creates variables that complement initial input vectors for regression problems by discretizing continuous values into intervals and training classifiers to predict value thresholds. This enriched vector is concatenated with the initial vector, serving as a generic pre-processing tool. The method was tested on 5 regressor types and evaluated in 33 regression datasets, demonstrating its effectiveness. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to add more information to the data before doing a regression analysis. They did this by breaking continuous values into smaller groups and using special tools called classifiers to decide which group each value belongs to. This creates a new vector with extra information that helps improve the regression results. The team tested their method on many different types of regressors and datasets, showing it can be useful for a wide range of problems. |
Keywords
* Artificial intelligence * Regression