Bank Note Authentication Data Set

Description

Bank Note Authentication Data Set is used to show the functionality of nonet package. This Data Set has below attribute information as variance of Wavelet Transformed image (continuous), skewness of Wavelet Transformed image (continuous), curtosis of Wavelet Transformed image (continuous),entropy of image (continuous), class (integer).

Usage

data("banknote_authentication")

Format

A data frame with 1372 observations on the following 5 variables.

variance

a numeric vector

skewness

a numeric vector

curtosis

a numeric vector

entropy

a numeric vector

class

a numeric vector

Details

Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extract features from images.

Source

This DataSet is fetched from UCI Website. URL is https://archive.ics.uci.edu/ml/datasets/banknote+authentication

References

Owner of database: Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe, volker.lohweg '@' hs-owl.de) Donor of database: Helene Dörksen (University of Applied Sciences, Ostwestfalen-Lippe, helene.doerksen '@' hs-owl.de) Date received: August, 2012

Examples

data(banknote_authentication)
dataframe <- data.frame(banknote_authentication)
head(dataframe)
str(banknote_authentication)

Ensemble Prediction without using training labels

Description

Ensemble Prediction without using training labels

Usage

nonet_ensemble(object, best_modelname)

Arguments

Value

A list of ensembled predictions. You can evaluate the performance of ensembled prediction using the evaulation matrix as Confusion matrix or AUROC.

Examples

# nonet_ensemble functionality can be explained via below example
# Setup
library(caret)
library(nonet)
library(rlist)

# Load Data
dataframe <- data.frame(banknote_authentication[600:900, ])
dataframe$class <- as.factor(ifelse(dataframe$class >= 1, 'Yes', 'No'))

# First Model
# Spliting into train and test
index <- createDataPartition(dataframe$class, p=0.75, list=FALSE)
trainSet <- dataframe[ index,]
testSet <- dataframe[-index,]

#Feature selection 
control <- rfeControl(functions = rfFuncs,
  method = "repeatedcv",
  repeats = 1,
  verbose = FALSE)

outcomeName <- 'class'
predictors <- c("variance", "skewness")

banknote_rf <- train(trainSet[,predictors],trainSet[,outcomeName],method='rf')
preds_rf_first <- predict.train(object=banknote_rf,testSet[,predictors],type="prob")
preds_rf_first_raw <- predict.train(object=banknote_rf,testSet[,predictors],type="raw")

# Second Model
# Spliting into train and test
index <- createDataPartition(dataframe$class, p=0.75, list=FALSE)
trainSet <- dataframe[ index,]
testSet <- dataframe[-index,]

#Feature selection 
control <- rfeControl(functions = rfFuncs,
  method = "repeatedcv",
  repeats = 2,
  verbose = FALSE)

outcomeName <- 'class'
predictors <- c("curtosis", "entropy")

banknote_rf <- train(trainSet[,predictors],trainSet[,outcomeName],method='rf')
preds_rf_second <- predict.train(object=banknote_rf,testSet[,predictors],type="prob")
preds_rf_second_raw <- predict.train(object=banknote_rf,testSet[,predictors],type="raw")

Stack_object <- list(preds_rf_first$Yes, preds_rf_second$Yes)
names(Stack_object) <- c("model_rf_first", "model_rf_second")

# Prediction using nonet_ensemble function
prediction_nonet <- nonet_ensemble(Stack_object, "model_rf_second")

Plot the predictions or results of nonet_ensemble

Description

Plot the predictions or results of nonet_ensemble

Usage

nonet_plot(x, y, dataframe, plot_type = NULL, nonet_size = 20,
  nonet_alpha = 0.3, nonet_bins = 25)

Arguments

Value

plotted for the plot results provided as input.

Examples

# nonet_plot functionality can be explained via below example
# Setup
library(caret)
library(nonet)
library(ggplot2)

# Load Data
dataframe <- data.frame(banknote_authentication[600:900, ])
dataframe$class <- as.factor(ifelse(dataframe$class >= 1, 'Yes', 'No'))

# Spliting into train and test
index <- createDataPartition(dataframe$class, p=0.75, list=FALSE)
trainSet <- dataframe[ index,]
testSet <- dataframe[-index,]

# Feature selection 
 control <- rfeControl(functions = rfFuncs,
  method = "repeatedcv",
  repeats = 2,
  verbose = FALSE)

outcomeName <- 'class'
predictors <- c("curtosis", "entropy")

# Model Training & predictions
banknote_rf <- train(trainSet[,predictors],trainSet[,outcomeName],method='rf')
predictions_rf_raw <- predict.train(object=banknote_rf,testSet[,predictors],type="raw")

# Results
nonet_eval_rf <- confusionMatrix(predictions_rf_raw,testSet[,outcomeName])
eval_rf_df <- data.frame(nonet_eval_rf$table)
nonet_plot(eval_rf_df$Prediction, eval_rf_df$Reference, eval_rf_df, plot_type = "point")