This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 356
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #307472
Title: An Improved Genetic Algorithm for Boosting in the Presence of Outliers
Author(s): Dong-Yop Oh* and J. Brian Gray+
Companies: The University of Alabama and The University of Alabama
Address: Dept of Info Systems, Statistics, and Mgt Science, Tuscaloosa, AL, 35487-0226,
Keywords: AdaBoost ; classification ; decision tree ; predictive modeling ; weak classifier
Abstract:

AdaBoost is one of the best known techniques for improving the accuracy of predictions in the classification problem. However, AdaBoost is prone to overfitting, especially for noisy data, and the final model is difficult to interpret. We describe an improved version of the GA-Boost algorithm (Oh and Gray, 2009), which directly solves for the weak classifiers and the weights of those weak classifiers using a genetic algorithm. The genetic algorithm utilizes a new penalized fitness function that limits the number of weak classifiers and controls the effects of outliers. We compare the test set error rates of GA-Boost to AdaBoost using several artificial and real world data sets.


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