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.
FuzzyGaussianNaiveBayesModel) with
$new(), $fit(), $predict() and
$set_params(). The functional interface
(FuzzyGaussianNaiveBayes(train, cl) +
predict()) still works and gives the same predictions.$fit()
tells you which model is being trained, on how much data, and how long
it took. Silence it with verbose = FALSE or
options(FuzzyClass.verbose = FALSE).fuzzy_naive_bayes() exposes all classifiers as engines,
ready for workflows, recipes,
rsample, tune and yardstick,
including tuning of fuzzy, alpha_cut and
ranking_method.n >= max(x),
class-wise sample sizes in the fuzzy-parameter confidence intervals and
nested alpha-cuts in PoiNBFuzzyParam.metd = 1:5) are vectorized over all
observations, about 100× faster than version 0.1.7.dplyr,
purrr, tibble, tidyr,
rlang, parallel, foreach and
doParallel are no longer required (cores is
kept only for backward compatibility).FuzzyClass provides 17 classifiers and
a fuzzy rule-based system, categorized by feature type and modeling
paradigm. Each classifier X has the functional interface
X(train, cl), the R6 class XModel and an
engine of fuzzy_naive_bayes():
| Classifier | Paradigm | Description | tidymodels engine |
|---|---|---|---|
FuzzyGaussianNaiveBayes |
Continuous | Gaussian Naive Bayes weighted by fuzzy membership degrees. | "gaussian" |
GauNBFuzzyParam |
Continuous | Gaussian Naive Bayes with fuzzy parameters (\(\tilde{\mu}, \tilde{\sigma}^2\)) across 5 estimation methods. | "gaussian_fuzzy_param" |
FuzzyExponentialNaiveBayes |
Continuous | Exponential Naive Bayes with fuzzy memberships for positive continuous features. | "exponential" |
ExpNBFuzzyParam |
Continuous | Exponential Naive Bayes with fuzzy rate parameters. | "exponential_fuzzy_param" |
FuzzyGammaNaiveBayes |
Continuous | Gamma Naive Bayes with fuzzy memberships for skewed continuous data. | "gamma" |
DWFuzzyGammaNaiveBayes |
Continuous | Double-Weighted Fuzzy Gamma Naive Bayes. | "dw_gamma" |
FuzzyBetaNaiveBayes |
Continuous | Beta Naive Bayes for continuous features bounded in \((0, 1)\) (rates/proportions). | "beta" |
FuzzyTriangularNaiveBayes |
Continuous | Naive Bayes with triangular membership functions. | "triangular" |
FuzzyTrapezoidalNaiveBayes |
Continuous | Naive Bayes with trapezoidal membership functions. | "trapezoidal" |
FuzzyPoissonNaiveBayes |
Discrete / Count | Poisson Naive Bayes with fuzzy memberships for integer count data. | "poisson" |
PoiNBFuzzyParam |
Discrete / Count | Poisson Naive Bayes with fuzzy rate parameters \(\tilde{\lambda}\). | "poisson_fuzzy_param" |
FuzzyBinomialNaiveBayes |
Discrete / Count | Binomial Naive Bayes with fuzzy memberships for binary/trial counts. | "binomial" |
FuzzyHipergeometricNaiveBayes |
Discrete / Count | Hypergeometric Naive Bayes for sampling without replacement from finite populations. | "hypergeometric" |
DWFuzzyHipergeometricNaiveBayes |
Discrete / Count | Double-Weighted Hypergeometric Naive Bayes. | "dw_hypergeometric" |
FuzzyGeoNaiveBayes |
Geometric | Naive Bayes with distance-based geometric fuzzy memberships. | "geometric" |
FuzzyNaiveBayes |
General | Generalized fuzzy Naive Bayes supporting continuous and categorical features. | "naive" |
FuzzyBayesRule |
Decision Rule | Fuzzy Bayes decision rule classifier. | "bayes_rule" |
FuzzyRuleBasedSystem |
Inference Rule | Fuzzy rule-based inference and classification system. | — |
You can install the released version of FuzzyClass from
CRAN:
install.packages("FuzzyClass")To install the latest development version:
# install.packages("devtools")
devtools::install_github("leapigufpb/FuzzyClass")FuzzyClass is designed with minimal, stable, and
battle-tested dependencies automatically installed by R:
The tidymodels packages
(parsnip, dials, tune, …) are
optional: install them to use fuzzy_naive_bayes().
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]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)
#> ℹ Gaussian Naive Bayes with Fuzzy Parameters: 420 observations, 3 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [31ms]
#>
# 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-02FuzzyGaussianNaiveBayes)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)
#> ℹ Fuzzy Gaussian Naive Bayes (Zadeh-based): 420 observations, 3 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [16ms]
#>
# 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 50Every classifier is an R6 class. Create the model, train it (a message tells you what is happening) and predict:
model <- FuzzyGammaNaiveBayesModel$new(fuzzy = TRUE)
model$fit(train = X_train, cl = y_train)
#> ℹ Fuzzy Gamma Naive Bayes: 420 observations, 3 predictors and 3 classes
#> ℹ Training the model: estimating its parameters
#> ✔ Model trained successfully [6ms]
#>
model
#> <FuzzyGammaNaiveBayes> Fuzzy Gamma Naive Bayes
#> ✔ Trained on 420 observations in 0.002s
#> Hyperparameters: fuzzy = TRUE
#> Predictors (3): V1, V2, V3
#> Classes (3): 1, 2, 3
head(model$predict(X_test))
#> [1] 1 1 1 1 1 1
#> Levels: 1 2 3
head(model$predict(X_test, type = "prob"))
#> 1 2 3
#> [1,] 0.9084371 9.153798e-02 2.487652e-05
#> [2,] 0.9998998 1.002413e-04 6.140526e-11
#> [3,] 0.9942020 5.797331e-03 7.118546e-07
#> [4,] 0.9999186 8.141746e-05 4.390692e-11
#> [5,] 0.8022878 1.976974e-01 1.476847e-05
#> [6,] 0.9729373 2.706046e-02 2.218717e-06Hyperparameters can be changed with $set_params(), and
the calls can be chained:
model$set_params(fuzzy = FALSE)$fit(X_train, y_train, verbose = FALSE)
mean(model$predict(X_test) == y_test)
#> [1] 0.8777778fuzzy_naive_bayes() is a parsnip model whose engines
are the FuzzyClass classifiers, so it plugs into workflows, recipes,
resampling and tuning:
library(tidymodels)
vr <- VirtualRealityData |>
as.data.frame() |>
mutate(V4 = factor(V4))
set.seed(123)
vr_split <- initial_split(vr, prop = 0.7, strata = V4)
spec <- fuzzy_naive_bayes(alpha_cut = tune(), ranking_method = tune()) |>
set_engine("gaussian_fuzzy_param")
wf <- workflow() |>
add_formula(V4 ~ .) |>
add_model(spec)
options(FuzzyClass.verbose = FALSE) # quiet while tuning
tuned <- tune_grid(wf, resamples = vfold_cv(training(vr_split), v = 5, strata = V4), grid = 5)
show_best(tuned, metric = "accuracy", n = 3)
#> # A tibble: 3 × 8
#> alpha_cut ranking_method .metric .estimator mean n std_err .config
#> <dbl> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
#> 1 0.0001 2 accuracy multiclass 0.788 5 0.0121 pre0_mod1_po…
#> 2 0.0501 1 accuracy multiclass 0.786 5 0.00842 pre0_mod3_po…
#> 3 0.0750 5 accuracy multiclass 0.786 5 0.00842 pre0_mod4_po…
final <- finalize_workflow(wf, select_best(tuned, metric = "accuracy")) |>
last_fit(vr_split)
collect_metrics(final)
#> # A tibble: 3 × 4
#> .metric .estimator .estimate .config
#> <chr> <chr> <dbl> <chr>
#> 1 accuracy multiclass 0.783 pre0_mod0_post0
#> 2 roc_auc hand_till 0.918 pre0_mod0_post0
#> 3 brier_class multiclass 0.156 pre0_mod0_post0Contributions, bug reports, and suggestions are very welcome!
git checkout -b feature/my-new-feature.git commit -am 'Add new feature'.git push origin feature/my-new-feature.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()).