Inverted Heuristics in Subgroup Discovery

author: Anita Valmarska, Department of Knowledge Technologies, Jožef Stefan Institute
published: Oct. 21, 2015,   recorded: October 2015,   views: 1563


Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography


In rule learning, rules are typically induced in two phases, rule refinement and rule selection. It was recently argued that the usage of two separate heuristics for each phase—in particular using the so-called inverted heuristic in the refinement phase—produces longer rules with comparable classifi- cation accuracy. In this paper we test the utility of inverted heuristics in the context of subgroup discovery. For this purpose we developed a DoubleBeam subgroup discovery algorithm that allows for combining various heuristics for rule refinement and selection. The algorithm was experimentally evaluated on 20 UCI datasets using 10-fold double-loop cross validation. The experimental results suggest that a variant of the DoubleBeam algorithm using a specific combination of refinement and selection heuristics generates longer rules without compromising rule quality. However, the DoubleBeam algorithm using inverted heuristics does not outperform the standard CN2-SD and SD algorithms.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: