A hybrid feature selection method for classifying metagenomic data in relation to inflammatory bowel disease
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Association for Computing Machinery
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Due to the recent advances in high throughput metagenomic sequencing technologies, Microbial abundance profiles of environmental samples have become publicly available. Increasing number of metagenomic studies has associated the imbalance of bacterial abundance to health and disase state of the host. This suggests utilizing the bacterial profiles as a diagnostic tool to identify the bacterial-related disease state of individuals. However, the high dimensional nature of metagenomic datasets renders this process a challenging task. Therefore, an efficient framework that enables accurate classification of metagenomic samples belonging to different classes is of central important. In this work, a hybrid feature selection technique that combines the advantages of filter and wrapper feature selection algorithms is proposed. The experimental results demonstrate that the proposed algorithm outperforms widely used feature selection techniques in terms of classification accuracy and provide a significant reduction in the computation time. © 2019 ACM.
Açıklama
University of Northumbria at Newcastle
3rd International Conference on Advances in Artificial Intelligence, ICAAI 2019 -- 26 October 2019 through 28 October 2019 -- -- 157135
3rd International Conference on Advances in Artificial Intelligence, ICAAI 2019 -- 26 October 2019 through 28 October 2019 -- -- 157135
Anahtar Kelimeler
Classification, Hybrid Feature Selection, Metagenomics
Kaynak
ACM International Conference Proceeding Series
WoS Q Değeri
Scopus Q Değeri
N/A
