Abstract

Selecting an effective method for combining the votes of classifiers in an ensemble can have a significant impact on the overall classification accuracy an ensemble is able to achieve. With some methods, the ensemble cannot even achieve as high a classification accuracy as the most accurate individual classifying component. To address this issue, we present the strategy of Heuristic Weighted Voting, a technique that uses heuristics to determine the confidence that a classifier has in its predictions on an instance by instance basis. Using these heuristics to weight the votes in an ensemble results in an overall average increase in classification accuracy over when compared to the most accurate classifier in the ensemble. When considering performance over 18 data sets, Heuristic Weighted Voting compares favorably both in terms of average classification accuracy and algorithm-by-algorithm comparisons in accuracy when evaluated against three baseline ensemble creation strategies as well as the methods of stacking and arbitration.

Degree

MS

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2007-10-25

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd2120

Keywords

machine learning, ensembles, confidence heuristics

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