Figure 7-4 Support vector machine: (a) two-dimensional
hyperplane, (b) with support vectors.
shown in Figure 7-4(a), the line that divides the target values
Attriter and Non-attriter is called a hyperplane . A hyperplane exists as
a complex equation that divides the space using N attribute
dimensions, where N is the number of predictor attributes. To
understand the concept of support vectors we look at two-dimensional
space. In Figure 7-4(b), the hyperplane that classifies Attriters from
Non-attriters and those data points that the margin pushes up
against are called support vectors . Margin is the minimal distance
between the data points and the hyperplane that divides Attriters
and Non-attriters .
SVM allows the selection of a kernel function. Kernel functions
morph data into high-dimensional vector space and look for relations
in such space. Many kernel functions have been introduced in the
data mining community. JDM includes kLinear, kGaussian, hypertangent,
polynomial, and sigmoid . For more details about the SVM and kernel
functions refer to [Cristianini/Shawe-Taylor 2000].
Feed Forward Neural Networks
Multilayer feed-forward neural networks with a back propagation
learning algorithm are one of the most popular neural network
techniques used for supervised learning. Despite the fact that neural
networks often take longer to build than other algorithms and they
do not provide interpretable results, they are popular for their
predictive accuracy and high tolerance to noisy data.
A neural network is an interconnected group of simulated neurons that
represent a computational model for information processing. A sim-
ulated neuron is a mathematical model that can take one or more
inputs to produce one output. The output is calculated by multiply-
ing each input by a corresponding weight , and combining them to