Classifiers Module#

The ex_fuzzy.classifiers module provides the main classification interface for the ex-fuzzy library.

Overview#

This module contains the high-level classifier that combines rule mining and genetic optimization for fuzzy classification tasks.

Classes#

RuleMineClassifier#

Examples#

Basic Usage#

from ex_fuzzy.classifiers import RuleMineClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load data
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create and train classifier
classifier = RuleMineClassifier(nRules=20, nAnts=4, verbose=True)
classifier.fit(X_train, y_train)

# Make predictions
y_pred = classifier.predict(X_test)
accuracy = classifier.score(X_test, y_test)
print(f"Accuracy: {accuracy:.3f}")

See Also#

  • ex_fuzzy.evolutionary_fit : Underlying genetic optimization

  • ex_fuzzy.rule_mining : Rule mining functionality

  • ex_fuzzy.fuzzy_sets : Fuzzy set definitions