Software to build Decision Trees for imbalanced data.
You can install the released version of ImbTreeAUC from GitHub with:
library(devtools)
install_github("KrzyGajow/ImbTreeAUC")This is a basic example which shows you how to solve a common problem:
library("ImbTreeAUC")
data(iris)
# Original dataset, multiclass classification, only numeric attributes
iris
# Predicting 1 as 0 will be penalized 5 times
class_cost_bin <- matrix( c(0,5,1,0), 2, 2, dimnames = list( 0:1, 0:1 ) )
# Predicting Setosa is very easy, Versicolor will have cost 5 for Virginica, Virginica will have cost 10 for Versicolor
class_cost_mult <- matrix( c(0,1,1,1,0,10,1,5,0), 3, 3, dimnames = list( levels(iris$Species), levels(iris$Species) ) )
# Assigning higher weights to those observation which are hard to correctly predict
obs_weights <- c( rep(1, 50), c( rep(1, 20), 5, rep(1, 6), 5, rep(1, 22) ), c( rep(1, 19), 10, rep(1, 9), 10, rep(1, 3), 10, 10, rep(1 ,15) ) )
# Dataset for binary classification, only numeric attributes
iris_2 <- iris
iris_2$Species <- factor( rep(0:1, each = 75) )
# Dataset for binary classification, with one unordered factor attribute
iris_3 <- iris_2
iris_3$Petal.Length <- factor( iris_3$Petal.Length )
# Dataset for binary classification, with one ordered factor attribute
iris_4 <- iris_2
iris_4$Petal.Length <- factor( iris_4$Petal.Length, ordered = T )
# Simulation 1: Original dataset, semi-global AUC
Tree1 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5, min_obs = 5,
type = "AUCs", levelPositive = "1", cp = 0, n_cores = 1, weights = NULL, cost = NULL,
AUCweight = "none", class_th = "equal", overfit = "leafcut", cf = 0.25)
PrintTree(Tree1)
Tree1_pred <- PredictTree(Tree1, iris)
confusionMatrix( Tree1_pred$Class, iris$Species )
# Simulation 2: Original dataset, semi-global AUC, adding observation weights
Tree2 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5, min_obs = 5,
type = "AUCs", levelPositive = "1", cp = 0, n_cores = 1, weights = obs_weights, cost = NULL,
AUCweight = "none", class_th = "equal", overfit = "leafcut", cf = 0.25)
PrintTree(Tree2)
Tree2_pred <- PredictTree(Tree2, iris)
confusionMatrix( Tree2_pred$Class, iris$Species )
# Simulation 3: Original dataset, semi-global AUC, adding observation weights, AUC weighting by size
Tree3 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5, min_obs = 5,
type = "AUCs", levelPositive = "1", cp = 0, n_cores = 1, weights = obs_weights, cost = NULL,
AUCweight = "bySize", class_th = "equal", overfit = "leafcut", cf = 0.25)
PrintTree(Tree3)
Tree3_pred <- PredictTree(Tree3, iris)
confusionMatrix( Tree3_pred$Class, iris$Species )
# Simulation 4: Original dataset, semi-global AUC, cost matrix
Tree4 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5, min_obs = 5,
type = "AUCs", levelPositive = "1", cp = 0, n_cores = 1, weights = NULL, cost = class_cost_mult,
AUCweight = "none", class_th = "equal", overfit = "leafcut", cf = 0.25)
PrintTree(Tree4)
Tree4_pred <- PredictTree(Tree4, iris)
confusionMatrix( Tree4_pred$Class, iris$Species )
# Simulation 5: Original dataset, semi-global AUC, cost matrix, AUC weighting by cost matrix
Tree5 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5, min_obs = 5,
type = "AUCs", levelPositive = "1", cp = 0, n_cores = 1, weights = NULL, cost = class_cost_mult,
AUCweight = "byCost", class_th = "equal", overfit = "leafcut", cf = 0.25)
PrintTree(Tree5)
Tree5_pred <- PredictTree(Tree5, iris)
confusionMatrix( Tree5_pred$Class, iris$Species )
# Simulation 6: Original dataset, semi-global AUC, pre-pruning based on the cp parameter
Tree6 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5, min_obs = 5,
type = "AUCs", levelPositive = "1", cp = 0.1, n_cores = 1, weights = NULL, cost = NULL,
AUCweight = "none", class_th = "equal", overfit = "none", cf = 0.25)
PrintTree(Tree6)
Tree6_pred <- PredictTree(Tree6, iris)
confusionMatrix( Tree6_pred$Class, iris$Species )
# Simulation 7: Original dataset, global AUC, without any prunning method
Tree7 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5, min_obs = 5,
type = "AUCg", levelPositive = "1", cp = 0, n_cores = 1, weights = NULL, cost = NULL,
AUCweight = "none", class_th = "equal", overfit = "none", cf = 0.25)
PrintTree(Tree7)
Tree7_pred <- PredictTree(Tree7, iris)
confusionMatrix( Tree7_pred$Class, iris$Species )
# Simulation 8: Original dataset, global AUC, post-pruning based on the cf parameter
Tree8 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5, min_obs = 5,
type = "AUCg", levelPositive = "1", cp = 0, n_cores = 1, weights = NULL, cost = NULL,
AUCweight = "none", class_th = "equal", overfit = "prune", cf = 0.25)
PrintTree(Tree8)
Tree8_pred <- PredictTree(Tree8, iris)
confusionMatrix( Tree8_pred$Class, iris$Species )
# Simulation 9: Original dataset, global AUC weighted by cost matrix, theoretical thresholds
Tree9 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5, min_obs = 5,
type = "AUCg", levelPositive = "1", cp = 0, n_cores = 1, weights = NULL, cost = class_cost_mult,
AUCweight = "none", class_th = "theoretical", overfit = "none", cf = 0.25)
PrintTree(Tree9)
Tree9_pred <- PredictTree(Tree9, iris)
confusionMatrix( Tree9_pred$Class, iris$Species )
# Simulation 10: Binary classification, global AUC, adding observation weights
Tree10 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris_2)[-ncol(iris_2)], data = iris_2, depth = 5, min_obs = 5,
type = "AUCg", levelPositive = "1", cp = 0, n_cores = 1, weights = obs_weights, cost = NULL,
AUCweight = "none", class_th = "equal", overfit = "leafcut", cf = 0.25)
PrintTree(Tree10)
Tree10_pred <- PredictTree(Tree10, iris_2)
confusionMatrix( Tree10_pred$Class, iris_2$Species, positive = "1" )
# Simulation 11: Binary classification, with one unordered factor attribute, theoretical thresholds, adding cost matrix, parallel processing
Tree11 <- ImbTreeAUC(Y_name = "Species", X_names = colnames(iris_3)[-ncol(iris_3)], data = iris_3, depth = 5, min_obs = 5,
type = "AUCg", levelPositive = "1", cp = 0, n_cores = 1, weights = NULL, cost = class_cost_bin,
AUCweight = "none", class_th = "equal", overfit = "leafcut", cf = 0.25)
PrintTree(Tree11)
Tree11_pred <- PredictTree(Tree11, iris_3)
confusionMatrix( Tree11_pred$Class, iris_3$Species, positive = "1" )
# Simulation 12: Interactive learning, original dataset, based on the probability peaks, 1 means that the whole tree is built based on the expert decision,
# top 4 splits on attribute level, default prunning based on the leaf cut
# Choosing sequence: 4, 3, 2, 1, 1, 1
Tree12 <- ImbTreeAUCInter(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5,
min_obs = 5, type = "AUCs", levelPositive = "1", cp = 0, n_cores = 1, weights = NULL,
AUCweight = "none", cost = NULL, class_th = "equal", overfit = "leafcut", cf = 0.25,
amb_prob = 1, top_split = 4, var_lev = T, amb_class = NULL, amb_class_freq = NULL )
PrintTreeInter(Tree12)
Tree12_pred <- PredictTree(Tree12, iris)
confusionMatrix( Tree12_pred$Class, iris$Species )
# Simulation 13: Interactive learning, original dataset, based on the probability peaks,
# top 4 splits on the attribute level, prunning based on the cp
# Choosing sequence: 4, 3, 3, 2, 1, 3, 1, 1, 1
Tree13 <- ImbTreeAUCInter(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5,
min_obs = 5, type = "AUCg", levelPositive = "1", cp = 0, n_cores = 1, weights = NULL,
AUCweight = "none", cost = NULL, class_th = "equal", overfit = "leafcut", cf = 0.25,
amb_prob = 1, top_split = 4, var_lev = T, amb_class = NULL, amb_class_freq = NULL )
PrintTreeInter(Tree13)
Tree13_pred <- PredictTree(Tree13, iris)
confusionMatrix( Tree13_pred$Class, iris$Species )
# Simulation 14: Interactive learning, original dataset, based on the class frequencies per node, 0 means that the whole tree is built based on the expert decision
# top 4 splits on the for each split of the attribute, default prunning based on the leaf cut,
# desired classes (versicolor, virginica) with the frequencies (0.5,0.1)
# Choosing sequence: 3, 2, 4, 1, 2, 1
Tree14 <- ImbTreeAUCInter(Y_name = "Species", X_names = colnames(iris)[-ncol(iris)], data = iris, depth = 5,
min_obs = 5, type = "AUCg", levelPositive = "1", cp = 0, n_cores = 1, weights = NULL,
AUCweight = "none", cost = NULL, class_th = "equal", overfit = "leafcut", cf = 0.25,
amb_prob = 1, top_split = 4, var_lev = T,
amb_class = c("versicolor", "virginica"), amb_class_freq = c(0.3,0.1) )
PrintTreeInter(Tree14)
Tree14_pred <- PredictTree(Tree14, iris)
confusionMatrix( Tree14_pred$Class, iris$Species )
# Simulation 15: Extracting rules
ExtractRules(Tree1)