Chapter 19. Classification Methods 4

Neural networks and support vector machines

Table of Contents

Solution of a linearly separable binary classification task by ANN and SVM
Using artificial neural networks (ANN)
Using support vector machines (SVM)

Solution of a linearly separable binary classification task by ANN and SVM

Description

In this experiment, we teach a perceptron as a simple artificial neural network (ANN) and a support vector machine ( SVM) on a linearly separable two dimensional dataset with two classes. The dataset is a subset of the Wine dataset. adatállomány egy részhalmaza. The classification accuracy of the classifiers is determined on the dataset.

Input

Wine [UCI MLR]

Figure 19.1. A linearly separable subset of the Wine dataset

A linearly separable subset of the Wine dataset

In order to apply the dataset for the experiment it have to go through a significant preprocessing. This involves choosing 2 attributes from the total 13 by using the Drop operator, then deleting the second class of the Class attribute and changing the measure level to binary by Metadata operator.

Output

The goodness of fit of the perceptron model can be checked by the usual statistics (misclassification rate, the number of incorrectly classified cases) and graphics (response and lift curve).

Figure 19.2. Fitting statistics for perceptron

Fitting statistics for perceptron

Figure 19.3. The classification matrix of the perceptron

The classification matrix of the perceptron

Figure 19.4. The cumulative lift curve of the perceptron

The cumulative lift curve of the perceptron

In the case of support vector machines (SVM), besides the above mentioned diagnostic tools, more details are given about the support vectors, namely the number of support vectors, the size of the margin and the list of support vectors.

Figure 19.5. Fitting statistics for SVM

Fitting statistics for SVM

Figure 19.6. The classification matrix of SVM

The classification matrix of SVM

Figure 19.7. The cumulative lift curve of SVM

The cumulative lift curve of SVM

Figure 19.8. List of the support vectors

List of the support vectors

Interpretation of the results

The figures and statistics show that the perceptron and support vector machine classify perfectly all of the traning cases as well.

Video

Workflow

sas_ann_svm_exp1.xml

Keywords

perceptron
supervised learning
classification

Operators

Data Source
Drop
Filter
Graph Explore
Metadata
Neural Network
Support Vector Machine