Neural Network Modeling using SAS Enterprise Miner introduces the readers to a non-linear modeling design called neural network modeling using SAS Enterprise Miner. The book will also familiarize the readers with this predictive and classification methodology in statistics called neural network modeling. This book is designed in making statisticians, researchers and programmers aware of the awesome new product now available in SAS® called Enterprise Miner. This first of its kind book will reveal the strength and ease of use of the powerful new module in SAS® with step-by-step instructions in creating a process flow diagram in preparation to data mining analysis and neural network predictive and classification modeling using SAS® Enterprise Miner v4.3.
A quick peek of the book
Contents of my Book to this Page
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An overview to traditional regression modeling with an explanation of the various assumptions that must be satisfied in both traditional regression modeling and neural network modeling with a brief summary of the various diagnostic statistics that are used to either detect extreme values or influential data points along with the various goodness-of-fit statistics that are used in determining the accuracy of the modeling fit.. |
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An overview to neural network modeling. The book will introduce the readers to the numerous configuration settings of the various layers in neural network modeling and the MLP architecture. The book will then briefly explain the various neural network architectural designs and the different combination, activation and error functions that can be applied in Enterprise Miner and the nonlinear modeling designs. The following sections will explain the hold out procedure that is typically performed in generating unbiased modeling estimates and the back-propagation iterative procedure that is used in calculating the weight estimates in neural network modeling. |
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An overview to the various optimization techniques along with numerical examples of the some of the optimization techniques that are used in Enterprise Miner and neural network modeling. |
| · | An overview to the various techniques in measuring the importance of the input or predictor variables to the neural network model. This will be followed by presenting the readers to various ideas in developing a well-designed neural network model and the advantages and disadvantages in neural network modeling. |
| · | An overview to the SAS neural network modeling procedure called the NEURAL procedure. |
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An overview to the powerful SAS product called Enterprise Miner. |
| · | Step-by-step instructions in constructing a Enterprise Miner process flow diagram in order to perform neural network predictive modeling and traditional regression modeling. |
| · | An overview to the configuration settings and listed results from the various nodes in Enterprise Miner that are used in neural network modeling and traditional regression modeling. |
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Comparing neural network predictive modeling estimates with the more popular statistical modeling estimates such as multiple linear regression modeling, logistic regression modeling, non-linear regression modeling, time series modeling and discriminant analysis based on various examples from SAS manuals and literature with an added overview to the corresponding statistical modeling designs and a brief explanation to the associated SAS modeling procedures, option statements and procedure output listings. |
“Neural
Network Modeling using SAS Enterprise Miner” introduces the readers to a
non-linear modeling design called neural network modeling using SAS
Enterprise Miner v4.3. This book will reveal the power and ease of use of
the powerful new module in SAS with step-by-step instructions in creating
a process flow diagram in preparation to data-mining analysis and neural
network statistical modeling.
Enterprise
Miner is specifically designed for data mining that has been available
since SAS version 8 was released. Data mining is an analytical tool that
is used in solving critical business decisions by analyzing large amounts
of data that is designed to discover relationships and unknown patterns in
the data. Enterprise Miner is built around the data mining SEMMA
methodology that is
specifically designed in handling enormous data sets in preparation to
subsequent data analysis. In SAS Enterprise Miner, the abbreviation SEMMA
stands for Sampling, Exploring, Modifying, Modeling and Assessing large
amounts of data. Neural network modeling with regard to the data mining
tasks falls under nonlinear regression modeling or classification
modeling. That is, neural network modeling can perform both predictive
modeling and classification modeling that depends on the variable that we
want to predict either being continuous or categorical.
There
are several advantages to using neural network
modeling. That is, it’s
extremely flexibility in modeling a wide variety of statistical models and
interpolating complex nonlinear functions. One of the advantages in neural network modeling is that it does not require any distributional
assumptions between the response variable
and
the predictor variables to the model.
That is, unlike traditional regression modeling, neural network modeling
does not require the use of a previously selected mathematical functional
form between the variable that we want to predict and the predictor
variables to the model. And yet, neural network modeling is extremely
accurate in interpolating a wide variety of functional forms assuming that
there are a sufficient number of hidden layer units, an adequate amount of
data and a reasonable amount of computational time. In classification
modeling, neural network modeling can approximate any decision boundary by assigning the given data
into distinct groups with great precision.
SAS
Enterprise Miner is a very easy to learn and very easy to use. Users do
not even need to know SAS programming and have very little statistical
expertise in designing an Enterprise Miner project in order to develop a
completely comprehensive statistical analysis reporting system. Yet, an
expert statistician can adjust the default settings and run the Enterprise
Miner process flow diagram
to their own personal
specifications. It takes advantage of the intuitive point-and-click
programming within a convenient graphic user interface. The diagram
workspace or the process flow diagram has the feel and same appearance
much like the desktop environment in Microsoft Windows. Users have various
icons to their disposal that will perform a wide variety of statistical
analysis. The nodes can be simply dragged onto the diagram workspace that
are then connected to one another all within the same graphical diagram
workspace.
This
book consists of step-by-step instructions along with an assortment of
illustrations for the reader to get familiar with the various nodes and
the corresponding working environment in SAS Enterprise Miner. There are numerous examples in explaining the various complex neural
network designs and optimization techniques used in neural network
modeling with numerous examples taken from various SAS literature
comparing the prediction estimates between both neural network and
traditional statistical modeling with an explanation to the listed results
that are generated between the models.
In
the introductory sections, the book will provide a brief description of
traditional regression modeling and the various assumptions that must be
satisfied. The following section of the book offers a graphical
illustration to the similarities between the parameter estimates in
multiple linear regression models and the weight estimates in neural
network models. Subsequent sections to the book introduces the reader to
the neural network design and the various
configuration settings such as the various neural network
layers, weight estimates, combination functions, transfer functions, objective or error functions and the optimization
techniques
that are used in neural
network modeling. “Neural Network Modeling using SAS Enterprise Miner”
will explain the various optimization techniques that are needed in
solving the non-linear neural network modeling design. Optimization
techniques such as the various line search
and grid search techniques
that are used within the neural network node in Enterprise Miner. It will
be followed by various numerical examples in order to simplify the
complexity of the various optimization techniques that are applied in
calculating the neural network weight estimates that are designed in
finding the smallest error to the neural network model. The book will then
explain the various ways in constructing a well-designed neural network
model and conclude the section in explaining to the readers of the
advantages and disadvantages to neural network modeling.
The
following section will then introduce the readers to SAS Enterprise Miner.
The section will begin with a brief overview to the SAS Enterprise Miner
interface that will explain the main menu option settings and the various
start-up configuration settings to the currently opened Enterprise Miner
project. The section will explain the basic steps in constructing
Enterprise Miner project and the corresponding process flow diagrams. The
book then leads into a general overview of the purpose of the available
nodes in Enterprise Miner for data mining analysis with regard to the
SEMMA design.
The
following chapter will explain the configuration settings and the
corresponding results that are generated from the Input Data Source node,
Data Partition node, Regression node, Neural Network node,
SAS Code node,
Assessment node and the Reporter node. These nodes are used to construct
the process flow diagram to compare neural network estimates with the
traditional statistical modeling designs. Each section of the book will
explain the purpose of the numerous tabs within each node along with the
various option settings that are listed within each tab of the
corresponding the node followed by an explanation of the results that are
generated from each one of the nodes.
The
final chapter to the book will compare the neural network estimates with the various
traditional statistical modeling designs such as linear and non-linear
regression modeling, logistic regression modeling, time series modeling
and discriminant analysis. Each section provides a brief summary of the
various statistical modeling designs and the associated SAS procedure and
option statements that are needed in producing the corresponding modeling
results. The various statistical modeling comparison examples that are
presented are from various SAS manuals and literature that will enable the
reader to cross-reference the statistical results. The book will compare
the accuracy in the fit between both models by graphically comparing the
modeling estimates and analyzing the various modeling assessment
statistics
from both models in
determining the best fit.
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