Support Vector Machines (SVM)

Following post shows an overview of Support Vector Machine (SVM) using the Wisconsin Breast Cancer Dataset, from UCI Machine Learning Repository. SVM generalize maximal margin classifier to non-linear class boundaries using hyperplanes to separate classes.
Wisconsin Breast Cancer Data, collected from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg, is composed of 11 cytological attributes computed from digitized images of a fine needle aspirate (FNA) of a breast mass. Collected cell attributes include clump thickness, uniformity of cell size and cell shape, marginal adhesion, single epithelial cell size, bare nuclei, bland chromatin, normal nucleoli and mitoses. Features are used to differentiate between benign and malignant samples, as defined by the Class predictor. For further details about feature definition and data collection, see publication.


Import packages

library(e1071)
library(plyr)


Reading and Cleaning Wisconsin Breast Cancer Dataset from UCI Machine Learning Repository

breast_cancer <- read.csv("https://raw.githubusercontent.com/azkajavaid/BreastCancerWisconsinData-UCI/master/BreastCancerData.txt?token=ANkFlyu6ncAg-xoOxqhgB6wSw0GRnuBOks5atcg2wA%3D%3D")
colnames(breast_cancer) <- c("Code", "ClumpThickness", "UniformCellSize", "UniformCellShape", "MarginalAdhesion", "EpithelialCellSize", "BareNuclei", "BlandChromatin", "NormalNucleoli", "Mitoses", "Class")
breast_cancer$Class <- as.character(breast_cancer$Class)
breast_cancer$Class <- revalue(breast_cancer$Class, c("2" = "Benign", "4" = "Malignant"))
breast_cancer$Class <- as.factor(breast_cancer$Class)
breast_cancer <- breast_cancer[!(breast_cancer$BareNuclei == "?"),] # dropping all observations with BareNuclei value of "?"
breast_cancer$BareNuclei <- as.integer(breast_cancer$BareNuclei)


Split data in training and test sets

n <- nrow(breast_cancer)
index = sample(1:n, size = round(0.75*n), replace = FALSE)
train = breast_cancer[index, ]
test = breast_cancer[-index, ]
paste("Observations in training data: ", nrow(train), sep = "")

## [1] "Observations in training data: 512"

paste("Observations in testing data: ", nrow(test), sep = "")

## [1] "Observations in testing data: 170"


Fit SVM model, predicting Class

svm_model <- svm(Class ~ ., data = train)
summary(svm_model)

## 
## Call:
## svm(formula = Class ~ ., data = train)
## 
## 
## Parameters:
##    SVM-Type:  C-classification 
##  SVM-Kernel:  radial 
##        cost:  1 
##       gamma:  0.1 
## 
## Number of Support Vectors:  93
## 
##  ( 64 29 )
## 
## 
## Number of Classes:  2 
## 
## Levels: 
##  Benign Malignant


Calculate accuracy for Class predictions on test data

predictions <- predict(svm_model, test)
svm_tab <- base::table(predictions, test$Class)
round(sum(diag(svm_tab))/sum(svm_tab), 3)

## [1] 0.971