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FSelector: a Ruby gem for feature selection
Author(s) -
Tiejun Cheng,
Yanli Wang,
Stephen H. Bryant
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts528
Subject(s) - computer science , feature selection , source code , normalization (sociology) , software , feature (linguistics) , data mining , selection (genetic algorithm) , open source , filter (signal processing) , artificial intelligence , programming language , linguistics , philosophy , sociology , anthropology , computer vision
The FSelector package contains a comprehensive list of feature selection algorithms for supporting bioinformatics and machine learning research. FSelector primarily collects and implements the filter type of feature selection techniques, which are computationally efficient for mining large datasets. In particular, FSelector allows ensemble feature selection that takes advantage of multiple feature selection algorithms to yield more robust results. FSelector also provides many useful auxiliary tools, including normalization, discretization and missing data imputation.

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