Last update: 14-02-2024
The classification predicament involves assigning labels or categories
to data instances based on observed features. Consider, for instance,
the task of discriminating between “spam” and “non-spam” emails. This
constitutes a classification task, where the algorithm must acquire the
ability to discern patterns that distinguish the two email types based
on their keywords, structure, or other attributes. Classification
algorithms employ a training dataset containing pre-labeled examples to
learn these patterns. When faced with unclassified data, the algorithm
applies the acquired patterns to predict the class to which they belong,
enabling efficient and precise automation in the categorization of new
cases. Algorithms like those in FuzzyClass
address this task by
leveraging data probabilities and characteristics, thus becoming
valuable tools for addressing intricate and ambiguous classification
problems.
A package manual that showcases the existing classifiers and demonstrates how to use it can be found at the following link: https://cran.r-project.org/package=FuzzyClass/FuzzyClass.pdf
Below is the list of packages on which FuzzyClass
depends. However,
during its installation, FuzzyClass
automatically installs the
dependencies:
# Installation
install.packages("devtools")
devtools::install_github("leapigufpb/FuzzyClass")
Once installed, you can load the FuzzyClass
package into your R
session:
# Package import
library(FuzzyClass)
To demonstrate the usage of FuzzyClass
, let’s look at reading and
preparing data:
library(FuzzyClass)
#' ---------------------------------------------
#' The following shows how the functions are used:
#' --------------
#' Reading a database:
#'
#' Actual training data:
data(VirtualRealityData)
VirtualRealityData <- as.data.frame(VirtualRealityData)
# Splitting into Training and Testing
split <- caTools::sample.split(t(VirtualRealityData[,1]), SplitRatio = 0.7)
Train <- subset(VirtualRealityData, split == "TRUE")
Test <- subset(VirtualRealityData, split == "FALSE")
# ----------------
test = Test[,-4]
Let’s delve into the example of using the Fuzzy Gaussian Naive Bayes
algorithm with fuzzy parameters:
# --------------------------------------------------
# Fuzzy Gaussian Naive Bayes with Fuzzy Parameters
fit_FGNB <- GauNBFuzzyParam(train = Train[,-4],
cl = Train[,4], metd = 2, cores = 1)
print(fit_FGNB)
#>
#> Fuzzy Gaussian Naive Bayes Classifier for Discrete Predictors
#>
#> Variables:
#> [1] "V1" "V2" "V3"
#> Class:
#> [1] "1" "2" "3"
saida <- predict(fit_FGNB, test)
Table <- table(factor(Test[,4]), saida)
Table
#> saida
#> 1 2 3
#> 1 50 5 2
#> 2 6 42 15
#> 3 0 11 49
#Accuracy:
sum(diag(Table))/sum(Table)
#> [1] 0.7833333
saidaMatrix <- predict(fit_FGNB, test, type = "matrix")
Additionally, you can visualize the results:
# --------------------------------------------------
# head view
saida |> head()
#> [1] 1 1 1 1 1 1
#> Levels: 1 2 3
saidaMatrix |> head()
#> 1 2 3
#> [1,] 0.9989435 0.001056437 9.262171e-08
#> [2,] 0.9939728 0.006011989 1.523144e-05
#> [3,] 0.8213097 0.116368282 6.232206e-02
#> [4,] 0.9946096 0.005386036 4.371040e-06
#> [5,] 0.8684685 0.069905455 6.162602e-02
#> [6,] 0.8015720 0.145765858 5.266218e-02
This code appears to be related to the application of a classification algorithm called “Fuzzy Gaussian Naive Bayes with Fuzzy Parameters.” An analysis of the steps present in the code:
fit_FGNB
):Train[,-4]
) and classes
(Train[,4]
), where the categorical response variable or label is
in column 4.predict
function is used to make predictions based on the
fitted model using the test set (test
).Table
) is created using the table
function. The confusion matrix compares the actual (expected)
classes with the classes predicted by the model.Overall, this code performs the training of a Fuzzy Gaussian Naive Bayes model with fuzzy parameters, makes predictions using the test set, creates a confusion matrix to evaluate the model’s performance, and calculates its accuracy.
This enhanced documentation provides a comprehensive guide to using the FuzzyClass package for probabilistic classification tasks. It covers installation, package usage, data preparation, and examples of applying the Fuzzy Gaussian Naive Bayes algorithm with fuzzy parameters. Feel free to explore the package further to leverage its capabilities for your classification tasks.
If you would like to contribute to FuzzyClass, please follow these steps:
FuzzyClass
repository on GitHub.FuzzyClass
repository.FuzzyClass
maintainers will review your pull request and may
ask you to make some changes before it is merged. Once your pull
request is merged, your contribution will be available to all
FuzzyClass
users.FuzzyClass
repository first to discuss your plans
with the maintainers.If you find a bug in FuzzyClass
, please report it by creating an issue
on the FuzzyClass
repository on GitHub at the link:
https://github.com/leapigufpb/FuzzyClass/issues. When reporting an
issue, please include the following information:
FuzzyClass
that
you are using.The FuzzyClass
maintainers will review your issue and may ask you for
more information before they can fix the bug. Once the bug is fixed, a
new release of FuzzyClass
will be made available.
Here are some additional tips for reporting issues to FuzzyClass:
I hope this helps! Let me know if you have any other questions.
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