Summary of Compact Nsga-ii For Multi-objective Feature Selection, by Sevil Zanjani Miyandoab et al.
Compact NSGA-II for Multi-objective Feature Selection
by Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 proposed Compact NSGA-II (CNSGA-II) algorithm is a binary optimization method that tackles feature selection as a multi-objective task, aiming to maximize classification accuracy while minimizing the number of selected features. By representing the population as probability distributions, CNSGA-II enhances evolutionary algorithms’ efficiency and reduces computational requirements. Instead of holding two populations, PVs generate new individuals by efficiently exploring the search space for non-dominated solutions. The algorithm’s compactness enables it to outperform NSGA-II in terms of hypervolume (HV) performance metric on five datasets, while requiring less memory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Feature selection is a crucial step in machine learning and data mining that helps remove irrelevant features, improving classification accuracy and reducing computational costs. This paper proposes a new algorithm called Compact NSGA-II (CNSGA-II) that selects the best features for a task while balancing performance and efficiency. CNSGA-II uses probability distributions to create new individuals, making it more memory-efficient than other methods. The results show that CNSGA-II performs better than another well-known method on five different datasets. |
Keywords
* Artificial intelligence * Classification * Feature selection * Machine learning * Optimization * Probability
