General: Ensembling, Stacking and Blending.Įnsemble Classifier Generators: Bagging, Random Subspace, SMOTE-Bagging, ICS-Bagging, SMOTE-ICS-Bagging.ĭynamic Selection: Overall Local Accuracy (OLA), Local Class Accuracy (LCA), Multiple Classifier Behavior (MCB), K-Nearest Oracles Eliminate (KNORA-E), K-Nearest Oracles Union (KNORA-U), A Priori Dynamic Selection, A Posteriori Dynamic Selection, Dynamic Selection KNN (DSKNN).Įnsemble Combination Rules: majority vote, min, max, mean and median.Įnsemble Diversity Metrics: Entropy Measure E, Kohavi Wolpert Variance, Q Statistics, Correlation Coefficient p, Disagreement Measure, Agreement Measure, Double Fault Measure.Įnsemble Pruning: Ensemble Pruning via Individual Contribution (EPIC).Įxample import numpy as np import matplotlib.pyplot as plt import idspec as gridspec import itertools import sklearn from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from brew.base import Ensemble, EnsembleClassifier from import EnsembleStack, EnsembleStackClassifier from import Combiner from mlxtend.data import iris_data from mlxtend.evaluate import plot_decision_regions # Initializing Classifiers clf1 = LogisticRegression ( random_state = 0 ) clf2 = RandomForestClassifier ( random_state = 0 ) clf3 = SVC ( random_state = 0, probability = True ) # Creating Ensemble ensemble = Ensemble () eclf = EnsembleClassifier ( ensemble = ensemble, combiner = Combiner ( 'mean' )) # Creating Stacking layer_1 = Ensemble () layer_2 = Ensemble () stack = EnsembleStack ( cv = 3 ) stack.

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