![]() ![]() ![]() Wrappers wrap around a specific learning method and conduct a search in the space of feature subset for optimal model performance. The latter also takes inter-feature correlation into account and generates a subset of the original feature set that tends to be both relevant and non-redundant. It provides a weight or ranking list as output, and features are usually selected based on a given threshold. The former measures independently the relevance of each feature to the target problem according to a certain evaluation criterion. Basically, there are two types of filters: filter-by-feature-weighting and filter-by-feature-searching. Filters investigate only the intrinsic characteristics of a given dataset and have the advantage of being fast as well as being independent of learning method. It proves to be effective in the data mining and bioinformatics fields for reducing dimensionality, selecting relevant and removing redundant features, increasing predictive accuracy and improving model interpretability ( Guyon and Elisseeff, 2003).ĭepending on how they interact with the learning method, various feature selection techniques roughly fall into three categories: filters, wrappers and embedded methods ( Guyon, 2006). The source code is available ( ) and is fully documented ( ).Ĭontact: or Information: Supplementary data are available at Bioinformatics online.įeature selection is of great importance for building statistical models when mining large datasets of high dimension, such as those generated from microarray and mass spectra analysis ( Saeys et al., 2007). #RUBY PG GEM VIEW RESULT INSTALL#FSelector is available from and can be installed like a breeze via the command gem install fselector. #RUBY PG GEM VIEW RESULT MAC OS#FSelector also provides many useful auxiliary tools, including normalization, discretization and missing data imputation.Īvailability: FSelector, written in the Ruby programming language, is free and open-source software that runs on all Ruby supporting platforms, including Windows, Linux and Mac OS X. In particular, FSelector allows ensemble feature selection that takes advantage of multiple feature selection algorithms to yield more robust results. FSelector primarily collects and implements the filter type of feature selection techniques, which are computationally efficient for mining large datasets. Summary: The FSelector package contains a comprehensive list of feature selection algorithms for supporting bioinformatics and machine learning research. ![]()
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