1. A pattern classifier based approach to Campaign planning
Gerrymandering
A pattern classifier based approach to Campaign planning

2. Goals Accurate prediction of votes in a region
Considering Campaign stops
Considering Advertising budget
Inexpensive implementation
No need for an on-hand ANN expert
No need to purchase expensive software package
No need to run on high-end workstation
“User-friendly” interface
Minimum of human interaction
Results are already interpreted, and presented in text-based format

3. Implementation Implemented in GNU Octave
Mixture of experts-type system
Result is weighted average of sub-classifiers
Weights are based on final Crate from training
Currently Implemented
MLP
KNN (order 3)
GMM
In progress
Fuzzy Classifier
SOM-based classifier

4. Data Currently Have Want Census data from 2000 and 1990
Obtained from
Vote results from (all presidential elections)
Obtained from National Records Archive
Want
Number of campaign stops per state (2000 and 1990)
Advertising dollars per state (2000 and 1990)

5. Difficulties (1/2) Census Campaign budget data
Different data collected
Stored in different format
Distributed through still different means
Not enough disk quota (on CAE workstations) or hard drive (on my PC) to store all of the data
Campaign budget data
Hard to collect (no central repository)
Differing formats
Usually not complete

6. Difficulties (2/2) Data is hard to parse No way to script retrieval
Some scripting of processing, but it still requires a lot of human interaction
Census data and vote data need to be manually combined
Vote data must be manually extracted from HTML tables