A few years ago in 2018, I discussed Boosted Configuration (neural) Networks (BCN for multivariate time series forecasting) in this document. Unlike Stochastic Configuration Networks from which they are inspired, BCNs aren’t randomized. Rather, they are closer to Gradient Boosting Machines and Matching Pursuit algorithms; with base learners being single-layered feedforward neural networks – that are actually optimized at each iteration of the algorithm.

The mathematician that you are has certainly been asking himself questions about the convexity of the problem at line 4, algorithm 1 (in the document). As of July 2022, there are unfortunately no answers to that question. BCNs works well empirically, as we’ll see, and finding the maximum at line 4 of the algorithm is achieved, by default, with R’s stats::nlminb. Other derivative-free optimizers are available in R package bcn.

As it will be shown in this document, BCNs can be used for classification. For this purpose, and as implemented in R package bcn, the response (variable to be explained) containing the classes is one-hot encoded as a matrix of probabilities equal to 0 or 1. Then, the classification technique dealing with a one-hot encoded response matrix is similar to the one presented in this post.

6 toy datasets are used for this basic demo of R package bcn: Iris, Wine, Ionosphere, Wisconsin Breast, Digits, Penguins. For each dataset, hyperparameter tuning has already been done. Repeated 5-fold cross-validation was carried out on 80% of the data, for each dataset, and the accuracy reported in the table below is calculated on the remaining 20% of the data. BCN results are compared to Random Forest’s (with default parameters), in order to verify that BCN results are not absurd – it’s not a competition between Random Forest and BCN here.

In the examples, you’ll also notice that BCN’s adjustment to a dataset can be relatively slow when the number of explanatory variables is high (>30, see the digits dataset example, initially with 64 covariates). This is because of line 4, algorithm 1, too: an optimization problem in a high dimensional space is repeated at each iteration of the algorithm.

The future for R package bcn (in no particular order)?

• Implement BCN for regression (a continuous response)
• Improve the speed of execution for high dimensional problems
• Implement a Python version
Dataset BCN test set Accuracy Random Forest test set accuracy
iris 100% 93.33%
Wine 97.22% 97.22%
Ionosphere 90.14% 95.77%
Breast cancer 99.12% 94.73%
Digits 97.5% 98.61%
Penguins 100% 100%

Content

Installing bcn From Github:

devtools::install_github("Techtonique/bcn")

# Browse the bcn manual pages
help(package = 'bcn')


Installing bcn from R universe:

# Enable repository from techtonique
options(repos = c(
techtonique = 'https://techtonique.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))

install.packages('bcn')

# Browse the bcn manual pages
help(package = 'bcn')


library(bcn) # Boosted Configuration networks
library(mlbench) # Machine Learning Benchmark Problems
library(caret)
library(randomForest)
library(pROC)


# 1 - iris dataset

data("iris")

head(iris)

dim(iris)

set.seed(1234)
train_idx <- sample(nrow(iris), 0.8 * nrow(iris))
X_train <- as.matrix(iris[train_idx, -ncol(iris)])
X_test <- as.matrix(iris[-train_idx, -ncol(iris)])
y_train <- iris$Species[train_idx] y_test <- iris$Species[-train_idx]

ptm <- proc.time()
fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 10L, nu = 0.335855,
lam = 10**0.7837525, r = 1 - 10**(-5.470031), tol = 10**-7,
activation = "tanh", type_optim = "nlminb", show_progress = TRUE)
cat("Elapsed: ", (proc.time() - ptm)[3])

plot(fit_obj$errors_norm, type='l')  preds <- predict(fit_obj, newx = X_test) mean(preds == y_test) table(y_test, preds)  set.seed(1234) rf <- randomForest::randomForest(x = X_train, y = y_train) mean(predict(rf, newdata=as.matrix(X_test)) == y_test)  print(head(predict(fit_obj, newx = X_test, type='probs'))) print(head(predict(rf, newdata=as.matrix(X_test), type='prob')))  # 2- wine dataset data(wine)  head(wine) dim(wine) set.seed(1234) train_idx <- sample(nrow(wine), 0.8 * nrow(wine)) X_train <- as.matrix(wine[train_idx, -ncol(wine)]) X_test <- as.matrix(wine[-train_idx, -ncol(wine)]) y_train <- as.factor(wine$target[train_idx])
y_test <- as.factor(wine$target[-train_idx])  ptm <- proc.time() fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 6L, nu = 0.8715725, lam = 10**0.2143678, r = 1 - 10**(-6.1072786), tol = 10**-4.9605713, show_progress = TRUE) cat("Elapsed: ", (proc.time() - ptm)[3])  plot(fit_obj$errors_norm, type='l')

preds <- predict(fit_obj, newx = X_test)

mean(preds == y_test)

table(y_test, preds)

set.seed(1234)
rf <- randomForest::randomForest(x = X_train, y = y_train)
mean(predict(rf, newdata=as.matrix(X_test)) == y_test)

print(head(predict(fit_obj, newx = X_test, type='probs')))


# 3 - Ionosphere dataset

data("Ionosphere")

head(Ionosphere)

dim(Ionosphere)

Ionosphere$V1 <- as.numeric(Ionosphere$V1)
Ionosphere$V2 <- NULL set.seed(1234) train_idx <- sample(nrow(Ionosphere), 0.8 * nrow(Ionosphere)) X_train <- as.matrix(Ionosphere[train_idx, -ncol(Ionosphere)]) X_test <- as.matrix(Ionosphere[-train_idx, -ncol(Ionosphere)]) y_train <- as.factor(Ionosphere$Class[train_idx])
y_test <- as.factor(Ionosphere$Class[-train_idx])  ptm <- proc.time() fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 50L, nu = 0.5182606, lam = 10**1.323274, r = 1 - 10**(-6.694688), col_sample = 0.7956659, tol = 10**-7, verbose=FALSE, show_progress = TRUE) cat("Elapsed: ", (proc.time() - ptm)[3])  plot(fit_obj$errors_norm, type='l')

preds <- predict(fit_obj, newx = X_test)

mean(preds == y_test)

table(y_test, preds)

set.seed(1234)
rf <- randomForest::randomForest(x = X_train, y = y_train)
mean(predict(rf, newdata=as.matrix(X_test)) == y_test)

print(head(predict(fit_obj, newx = X_test, type='probs')))

roc_obj <- pROC::roc(as.numeric(y_test), as.numeric(preds))
pROC::auc(roc_obj)

roc_obj_rf <- pROC::roc(as.numeric(y_test), as.numeric(predict(rf, newdata=as.matrix(X_test))))
pROC::auc(roc_obj_rf)


# 4 - breast cancer dataset

data("breast_cancer")

head(breast_cancer)

dim(breast_cancer)

set.seed(1234)
train_idx <- sample(nrow(breast_cancer), 0.8 * nrow(breast_cancer))
X_train <- as.matrix(breast_cancer[train_idx, -ncol(breast_cancer)])
X_test <- as.matrix(breast_cancer[-train_idx, -ncol(breast_cancer)])
y_train <- as.factor(breast_cancer$target[train_idx]) y_test <- as.factor(breast_cancer$target[-train_idx])

ptm <- proc.time()
fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 31L, nu = 0.4412851,
lam = 10**-0.2439358, r = 1 - 10**(-7), col_sample = 0.5, tol = 10**-2, show_progress = TRUE)
cat("Elapsed: ", (proc.time() - ptm)[3])

plot(fit_obj$errors_norm, type='l')  preds <- predict(fit_obj, newx = X_test) mean(preds == y_test) table(y_test, preds)  set.seed(1234) rf <- randomForest::randomForest(x = X_train, y = y_train) mean(predict(rf, newdata=as.matrix(X_test)) == y_test)  print(head(predict(fit_obj, newx = X_test, type='probs'))) print(head(predict(rf, newdata=as.matrix(X_test), type='prob')))  roc_obj <- pROC::roc(as.numeric(y_test), as.numeric(preds)) pROC::auc(roc_obj)  roc_obj_rf <- pROC::roc(as.numeric(y_test), as.numeric(predict(rf, newdata=as.matrix(X_test)))) pROC::auc(roc_obj_rf)  # 5 - digits dataset data("digits")  head(digits) dim(digits) set.seed(1234) train_idx <- sample(nrow(digits), 0.8 * nrow(digits)) X_train <- as.matrix(digits[train_idx, -ncol(digits)]) X_test <- as.matrix(digits[-train_idx, -ncol(digits)]) y_train <- as.factor(digits$target[train_idx])
X_train <- X_train[, -caret::nearZeroVar(X_train)]
y_test <- as.factor(digits$target[-train_idx]) X_test <- X_test[, colnames(X_train)]  ptm <- proc.time() fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 50L, nu = 0.6549268, lam = 10**0.4635435, r = 1 - 10**(-7), col_sample = 0.8928518, tol = 10**-5.483609, verbose=FALSE, show_progress = TRUE) cat("Elapsed: ", (proc.time() - ptm)[3])  plot(fit_obj$errors_norm, type='l')

preds <- predict(fit_obj, newx = X_test)

mean(preds == y_test)

table(y_test, preds)

set.seed(1234)
rf <- randomForest::randomForest(x = X_train, y = y_train)
mean(predict(rf, newdata=as.matrix(X_test)) == y_test)

print(head(predict(fit_obj, newx = X_test, type='probs')))


# 6 - Penguins dataset

data("penguins")

penguins_ <- as.data.frame(penguins)

replacement <- median(penguins$bill_length_mm, na.rm = TRUE) penguins_$bill_length_mm[is.na(penguins$bill_length_mm)] <- replacement replacement <- median(penguins$bill_depth_mm, na.rm = TRUE)
penguins_$bill_depth_mm[is.na(penguins$bill_depth_mm)] <- replacement

replacement <- median(penguins$flipper_length_mm, na.rm = TRUE) penguins_$flipper_length_mm[is.na(penguins$flipper_length_mm)] <- replacement replacement <- median(penguins$body_mass_g, na.rm = TRUE)
penguins_$body_mass_g[is.na(penguins$body_mass_g)] <- replacement

# replacing NA's by the most frequent occurence
penguins_$sex[is.na(penguins$sex)] <- "male" # most frequent

print(summary(penguins_))
print(sum(is.na(penguins_)))

# one-hot encoding for covariates
penguins_mat <- model.matrix(species ~., data=penguins_)[,-1]
penguins_mat <- cbind(penguins_$species, penguins_mat) penguins_mat <- as.data.frame(penguins_mat) colnames(penguins_mat)[1] <- "species" print(head(penguins_mat)) print(tail(penguins_mat)) y <- as.integer(penguins_mat$species)
X <- as.matrix(penguins_mat[,2:ncol(penguins_mat)])

n <- nrow(X)
p <- ncol(X)

set.seed(1234)
index_train <- sample(1:n, size=floor(0.8*n))
X_train <- X[index_train, ]
y_train <- factor(y[index_train])
X_test <- X[-index_train, ]
y_test <- factor(y[-index_train])

ptm <- proc.time()
fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 23, nu = 0.470043,
lam = 10**-0.05766029, r = 1 - 10**(-7.905866), tol = 10**-7,
show_progress = TRUE)
cat("Elapsed: ", (proc.time() - ptm)[3])

plot(fit_obj\$errors_norm, type='l')

preds <- predict(fit_obj, newx = X_test)

mean(preds == y_test)

table(y_test, preds)

set.seed(1234)
rf <- randomForest::randomForest(x = X_train, y = y_train)
mean(predict(rf, newdata=as.matrix(X_test)) == y_test)

print(head(predict(fit_obj, newx = X_test, type='probs')))