Summary of Kernel Density Estimation For Multiclass Quantification, by Alejandro Moreo et al.
Kernel Density Estimation for Multiclass Quantification
by Alejandro Moreo, Pablo González, Juan José del Coz
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The abstract presents a machine learning problem in several disciplines, including social sciences, epidemiology, sentiment analysis, and market research. The goal is to predict the distribution of classes in a population rather than individual labels. Quantification is the supervised task that achieves this, even with label shift. Distribution-matching (DM) approaches model populations using histograms of posterior probabilities. However, these approaches are suboptimal for multiclass settings as they miss inter-class information. The authors propose a new representation mechanism based on multivariate densities and kernel density estimation (KDE). Experiments show that their method, KDEy, outperforms previous DM approaches in quantification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the pattern of different groups in a big collection of things. Imagine you have a bunch of people and you want to know how many are happy or sad. You don’t just care about individual people, but how many happy people there are compared to sad ones. This problem is important for many fields like social sciences, medicine, and business. The current way to solve this problem has some limitations when dealing with multiple groups. So, the authors came up with a new idea to represent these groups in a more accurate way using something called kernel density estimation (KDE). They tested their method and found it works better than previous ways. |
Keywords
* Artificial intelligence * Density estimation * Machine learning * Supervised
