Summary of Fsdr: a Novel Deep Learning-based Feature Selection Algorithm For Pseudo Time-series Data Using Discrete Relaxation, by Mohammad Rahman et al.
FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation
by Mohammad Rahman, Manzur Murshed, Shyh Wei Teng, Manoranjan Paul
First submitted to arxiv on: 13 Mar 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 introduces Feature Selection through Discrete Relaxation (FSDR), a deep learning-based algorithm for selecting features from Pseudo Time-Series (PTS) data. FSDR learns important features as model parameters using discrete relaxation, which enables it to handle high-dimensional data. Unlike existing feature selection algorithms, FSDR is capable of accommodating a large number of feature dimensions. Experimental results on a hyperspectral dataset demonstrate that FSDR outperforms three commonly used feature selection algorithms in terms of execution time, R2, and RMSE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FSDR is a new way to pick the most important features from data that’s arranged like a sequence but doesn’t follow a normal timeline. It uses deep learning to find these important features, which helps it handle lots of data at once. The algorithm does better than other methods in finding the right features and getting good results. |
Keywords
* Artificial intelligence * Deep learning * Feature selection * Time series
