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COMPONENTS.md

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Classifiers

  • Adaptive Boosting (AdaBoost),
  • Bagging (Bagging),
  • Extremely Randomized Trees (ExtremelyRandomizedTrees),
  • Linear SVC (LinearSVC),
  • Multi Layer Perceptron (MultiLayerPerceptron),
  • Random Forest Classifier (RandomForest),
  • Decision Tree Classifier (DecisionTree),
  • K-Neighbors Classifier (KNeighbors),
  • Gaussian Process Classifier (GaussianProcess),
  • Gaussian Naive Bayes (GaussianNB),
  • Quadratic Discriminant Analysis (QuadraticDiscriminantAnalysis).

Feature Selection Algorithms

  • Select K Best (SelectKBest),
  • Select Percentile (SelectPercentile),
  • Variance Threshold (VarianceThreshold).

Nature-Inspired based

  • Bat Algorithm (BatAlgorithm),
  • Differential Evolution (DifferentialEvolution),
  • Self-Adaptive Differential Evolution (jDEFSTH),
  • Grey Wolf Optimizer (GreyWolfOptimizer),
  • Particle Swarm Optimization (ParticleSwarmOptimization).

Feature Transformation Algorithms

  • Normalizer (Normalizer),
  • Standard Scaler (StandardScaler),
  • Maximum Absolute Scaler (MaxAbsScaler),
  • Quantile Transformer (QuantileTransformer),
  • Robust Scaler (RobustScaler).

Fitness Functions based on

  • Accuracy (Accuracy),
  • Cohen's kappa (CohenKappa),
  • F1-Score (F1),
  • Precision (Precision).

Categorical Feature Encoders

  • One-Hot Encoder (OneHotEncoder).

Feature Imputers

  • Simple Imputer (SimpleImputer).