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Cited by in F6Publishing

For:	Bose S, Das C, Banerjee A, Ghosh K, Chattopadhyay M, Chattopadhyay S, Barik A. An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples. PeerJ Comput Sci 2021;7:e671. [PMID: 34616883 DOI: 10.7717/peerj-cs.671] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]

For:

Bose S, Das C, Banerjee A, Ghosh K, Chattopadhyay M, Chattopadhyay S, Barik A. An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples. PeerJ Comput Sci 2021;7:e671. [PMID: 34616883 DOI: 10.7717/peerj-cs.671] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]

Number	Citing Articles
1	Zhou G, Lee M, Wang X, Zhong D, Yan G. Mapping potential malaria vector larval habitats for larval source management: Introduction to multi-model ensembling approaches.. [DOI: 10.1101/2022.10.06.511086] [Reference Citation Analysis]
2	Jenul A, Schrunner S, Pilz J, Tomic O. A user-guided Bayesian framework for ensemble feature selection in life science applications (UBayFS). Mach Learn. [DOI: 10.1007/s10994-022-06221-9] [Reference Citation Analysis]
3	Rodríguez Alvarez Y, García Lorenzo MM, Caballero Mota Y, Filiberto Cabrera Y, García Hilarión IM, Machado Montes de Oca D, Bello Pérez R. Fuzzy prototype selection-based classifiers for imbalanced data. Case study. Pattern Recognition Letters 2022. [DOI: 10.1016/j.patrec.2022.07.003] [Reference Citation Analysis]