A Back Propagation Artificial Neural Networks Approach to Automatic Customer Record Classification

Maastricht University / Department of Quantitative Economics
Master Thesis
A Back Propagation Artificial Neural Networks Approach to Automatic Customer Record Classification.
Author: Camilo Gaviria

Introduction :

In this thesis I address the problem of finding the correct customer to account (AMID)
assignment based on several variables called customer attributes. These customer at-
tributes contain information that provide a partial description of the customer, i.e. cus-
tomer name, customer address, etc. As each customer record carries many attributes,
and there are many possible accounts or classes to which it can belong, this is in itself
a combinatorial optimization problem which is hard to solve. I will call the problem
of minimizing the number of incorrect class (AMID) assignments from now on, the
customer alignment problem.
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In the literature, many approach to address similar problems are discussed,
one of these, which I have chosen to explore, is the utilization of neural network based
classi cation approaches that are commonly used in the classi cation of natural language
documents as patents, articles, books. In this thesis I will approach the customer record
alignment problem using an adaptation of the methodology used by to classify
patent documents.

FannTool ;

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Another advantage of using this pre-compiled library, is the availability of a series of
open source graphical user interfaces. By using one of these GUI's, the development
efort is significantly reduced, and the number of additional tests and configurations
that can be easily performed is much larger. Taking into account that there are several
parameters for which there is no one setup rule (number of neurons per layer, number of layer, type of training algorithm, etc.) being able to easily change the configuration
of the ANN is an highly valued feature. The GUI of choice was FANNTool 1.3 an
open source GUI developed specifically for FANN and portable to several OS. This GUI
allows for direct instance scaling, data manipulation as well as real time monitoring of
the learning process.

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