Two Bagging Algorithms with Coupled Learners to Encourage Diversity
published: Oct. 8, 2007, recorded: September 2007, views: 3239
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In this paper, we present two ensemble learning algorithms which make use of boostrapping and out-of-bag estimation in an attempt to inherit the robustness of bagging to overfitting. As against bagging, with these algorithms learners have visibility on the other learners and cooperate to get diversity, a characteristic that has proved to be an issue of major concern to ensemble models. Experiments are provided using two regression problems obtained from UCI.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !