GerrymanderingGoalsImplementationDataDifficulties (1/2)Difficulties (2/2)GerrymanderingA pattern classifier based approach to Campaign planningGoals•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 formatImplementation•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 classifierData•Currently Have–Census data from 2000 and 1990•Obtained from http://www.census.gov/–Vote results from 1850-2000 (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)Difficulties (1/2)•Census–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 completeDifficulties (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
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