An efficient feature selection algorithm based on the description vector and hypergraph
Information Sciences, ISSN: 0020-0255, Vol: 629, Page: 746-759
2023
- 4Citations
- 9Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The “curse of dimensionality” is a bottleneck in big data and artificial intelligence. To reduce the dimensionality of data using the minimal vertex covers of graphs, a discernibility matrix can be applied to construct a hypergraph. However, constructing a hypergraph using a discernibility matrix is a time-consuming and memory-consuming task. To solve this problem, we propose a more efficient approach to graph construction based on a description vector. We develop a graph-based heuristic algorithm for feature selection, named the graph-based description vector (GDV) algorithm, which is designed for fast search and has lower time and space complexities than four existing representative algorithms. Numerical experiments have shown that, compared with these four algorithms, the average running time of the GDV algorithm is reduced by a factor of 36.81 to 271.54, while the classification accuracy is maintained at the same level.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0020025523000464; http://dx.doi.org/10.1016/j.ins.2023.01.046; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85148330809&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0020025523000464; https://dx.doi.org/10.1016/j.ins.2023.01.046
Elsevier BV
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