FuzzyClass

A comprehensive family of probability-based fuzzy and non-fuzzy classifiers.

Classification problems involve assigning categorical labels to data instances based on observed features. In real-world applications, classes often overlap, boundaries are imprecise, and measurements carry uncertainty. Traditional Bayesian classifiers assume rigid distributional parameters and crisp class assignments.

FuzzyClass addresses these challenges by unifying Fuzzy Set Theory ($\mu \in [0, 1]$) with Probabilistic Naive Bayes classification. By leveraging soft membership degrees, fuzzy parameters (triangular and trapezoidal), and geometric distance weighting, FuzzyClass delivers robust, interpretable, and high-performance classification for complex and ambiguous data.

A complete manual showcasing all classifiers and mathematical details is available at the CRAN FuzzyClass Reference Manual.

What’s New in Version 0.2.0

🐞 Correct class labelling: parameters and predicted labels now always follow the same class order (factor levels). Previous development code could mislabel predictions when the training data were not sorted by class.
📐 Estimation aligned with the literature: exact Gamma maximum likelihood, unbounded Beta shapes, Geometric pmf consistent with its estimator, Binomial n >= max(x), class-wise sample sizes in the fuzzy-parameter confidence intervals and nested alpha-cuts in PoiNBFuzzyParam.
⚡ Vectorized predictions: prediction and fuzzy-number ranking (metd = 1:5) are vectorized over all observations, about 100× faster than version 0.1.7.
🪶 Lighter dependencies: dplyr, purrr, tibble, tidyr, rlang, parallel, foreach and doParallel are no longer required (cores is kept only for backward compatibility).

Available Classifiers (Taxonomy)

FuzzyClass provides 18 dedicated classifiers categorized by feature type and modeling paradigm:

Classifier	Paradigm	Description
`FuzzyGaussianNaiveBayes`	Continuous	Gaussian Naive Bayes weighted by fuzzy membership degrees.
`GauNBFuzzyParam`	Continuous	Gaussian Naive Bayes with fuzzy parameters ($\tilde{\mu}, \tilde{\sigma}^2$) across 5 estimation methods.
`FuzzyExponentialNaiveBayes`	Continuous	Exponential Naive Bayes with fuzzy memberships for positive continuous features.
`ExpNBFuzzyParam`	Continuous	Exponential Naive Bayes with fuzzy rate parameters.
`FuzzyGammaNaiveBayes`	Continuous	Gamma Naive Bayes with fuzzy memberships for skewed continuous data.
`DWFuzzyGammaNaiveBayes`	Continuous	Double-Weighted Fuzzy Gamma Naive Bayes.
`FuzzyBetaNaiveBayes`	Continuous	Beta Naive Bayes for continuous features bounded in $(0, 1)$ (rates/proportions).
`FuzzyTriangNaiveBayes`	Continuous	Naive Bayes with triangular membership functions.
`FuzzyTrapeNaiveBayes`	Continuous	Naive Bayes with trapezoidal membership functions.
`FuzzyPoissonNaiveBayes`	Discrete / Count	Poisson Naive Bayes with fuzzy memberships for integer count data.
`PoiNBFuzzyParam`	Discrete / Count	Poisson Naive Bayes with fuzzy rate parameters $\tilde{\lambda}$.
`FuzzyBinomialNaiveBayes`	Discrete / Count	Binomial Naive Bayes with fuzzy memberships for binary/trial counts.
`FuzzyHipergeometricNaiveBayes`	Discrete / Count	Hypergeometric Naive Bayes for sampling without replacement from finite populations.
`DoubleWeightedFuzzyHipergeometricNaiveBayes`	Discrete / Count	Double-Weighted Hypergeometric Naive Bayes.
`FuzzyGeoNaiveBayes`	Geometric	Naive Bayes with distance-based geometric fuzzy memberships.
`FuzzyNaiveBayes`	General	Generalized fuzzy Naive Bayes supporting continuous and categorical features.
`FuzzyBayesRule`	Decision Rule	Fuzzy Bayes decision rule classifier.
`FuzzyRuleBasedSystem`	Inference Rule	Fuzzy rule-based inference and classification system.

Installation

Stable Version from CRAN

You can install the released version of FuzzyClass from CRAN:

install.packages("FuzzyClass")

Development Version from GitHub

To install the latest development version:

# install.packages("devtools")
devtools::install_github("leapigufpb/FuzzyClass")

Dependencies

FuzzyClass is designed with minimal, stable, and battle-tested dependencies automatically installed by R:

Quick Start & Examples

1. Data Preparation

We demonstrate package functionality using the built-in VirtualRealityData dataset:

library(FuzzyClass)

# Load dataset
data(VirtualRealityData)
df <- as.data.frame(VirtualRealityData)

# Split into Training (70%) and Testing (30%) sets
set.seed(123)
split <- caTools::sample.split(df[, 4], SplitRatio = 0.7)
Train <- subset(df, split == TRUE)
Test  <- subset(df, split == FALSE)

X_train <- Train[, -4]
y_train <- Train[, 4]
X_test  <- Test[, -4]
y_test  <- Test[, 4]

2. Gaussian Naive Bayes with Fuzzy Parameters (`GauNBFuzzyParam`)

Fit a Gaussian model where mean and variance are represented as fuzzy numbers:

# Fit model using fuzzy estimation method 2
fit_fgnb <- GauNBFuzzyParam(train = X_train, cl = y_train, metd = 2, cores = 1)

# Predict classes
pred_classes <- predict(fit_fgnb, X_test)

# Confusion Matrix
conf_mat <- table(Actual = y_test, Predicted = pred_classes)
conf_mat
#>       Predicted
#> Actual  1  2  3
#>      1 55  4  1
#>      2  8 34 18
#>      3  1  9 50

# Classification Accuracy
accuracy <- sum(diag(conf_mat)) / sum(conf_mat)
cat(sprintf("Test Accuracy: %.2f%%\n", accuracy * 100))
#> Test Accuracy: 77.22%

You can also extract the full posterior membership / probability matrix:

# Extract prediction probability matrix
prob_matrix <- predict(fit_fgnb, X_test, type = "matrix")
head(prob_matrix)
#>              1           2            3
#> [1,] 0.4991627 0.376053403 1.247839e-01
#> [2,] 0.9942344 0.005757106 8.478896e-06
#> [3,] 0.9818783 0.009466896 8.654851e-03
#> [4,] 0.9943634 0.005634324 2.298521e-06
#> [5,] 0.5559862 0.433612780 1.040104e-02
#> [6,] 0.9520142 0.013665813 3.432000e-02

3. Continuous Fuzzy Gaussian Naive Bayes (`FuzzyGaussianNaiveBayes`)

Fit a Gaussian Naive Bayes classifier where observations have fuzzy membership degrees:

# Fit standard fuzzy Gaussian Naive Bayes
fit_gnb <- FuzzyGaussianNaiveBayes(train = X_train, cl = y_train)

# Predict classes on test set
pred_gnb <- predict(fit_gnb, X_test)

# Confusion matrix
table(Actual = y_test, Predicted = pred_gnb)
#>       Predicted
#> Actual  1  2  3
#>      1 58  2  0
#>      2  7 50  3
#>      3  0 10 50

How to Contribute

Contributions, bug reports, and suggestions are very welcome!

Fork the FuzzyClass repository.
Create a feature branch: git checkout -b feature/my-new-feature.
Commit your changes: git commit -am 'Add new feature'.
Push to the branch: git push origin feature/my-new-feature.
Submit a Pull Request.

Reporting Issues

If you encounter any issues or unexpected behavior, please submit an issue on GitHub: 👉 https://github.com/leapigufpb/FuzzyClass/issues

Please provide a reproducible example (reprex) and session information (sessionInfo()).