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multiclassPairs
View on CRAN: Click
here
Download and install multiclassPairs package within the R console
Install from CRAN:
install.packages("multiclassPairs")
Install from Github:
library("remotes")
install_github("cran/multiclassPairs")
Install by package version:
library("remotes")
install_version("multiclassPairs", "0.4.3")
Attach the package and use:
library("multiclassPairs")
Maintained by
Nour-al-dain Marzouka
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[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.multiclassPairs . https://github.com/NourMarzouka/multiclassPairs . multiclassPairs results . multiclassPairs.pdf . Tutorial for multiclassPairs R package . multiclassPairs_0.4.3.tar.gz . multiclassPairs_0.4.3.zip . multiclassPairs_0.4.3.zip . multiclassPairs_0.4.3.zip . multiclassPairs_0.4.3.tgz . multiclassPairs_0.4.3.tgz . multiclassPairs_0.4.3.tgz . multiclassPairs_0.4.3.tgz . multiclassPairs_0.4.3.tgz . multiclassPairs_0.4.3.tgz . multiclassPairs archive . https://CRAN.R-project.org/package=multiclassPairs .
First Published: 2020-09-10
Latest Update: 2021-05-16
Description:
A toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on 'switchBox' package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret. For complex problems RF performs better. Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...). Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model. We provide a detailed description with each function in this package to explain the filtration and training methodology in each line. Reference: Marzouka & Eriksson (2021) .
How to cite:
Nour-al-dain Marzouka (2020). multiclassPairs: Build MultiClass Pair-Based Classifiers using TSPs or RF. R package version 0.4.3, https://cran.r-project.org/web/packages/multiclassPairs. Accessed 11 Apr. 2025.
Previous versions and publish date:
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Complete documentation for multiclassPairs
Functions, R codes and Examples using
the multiclassPairs R package
Some associated functions: ReadData . do_dunn_test . filter_genes_TSP . group_TSP . optimize_RF . plot_binary_RF . plot_binary_TSP . predict_RF . predict_one_vs_rest_TSP . print-methods . proximity_matrix_RF . sort_genes_RF . sort_rules_RF . summary_genes_RF . train_RF . train_one_vs_rest_TSP .
Some associated R codes: functions.R . Full multiclassPairs package functions and examples
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