Personalized Action Rules for Side Effects Object Grouping

Hakim Touati, Jieyan Kuang, Ayman Hajja, Zbigniew W. Ras

College of Computing and Informatics, University of North Carolina, Charlotte, USA.
Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland.

DOI: 10.4236/ijis.2013.31A004                

Abstract

There have been multiple techniques to discover action-rules, but the problem of triggering those rules was left exclusively to domain knowledge and domain experts. When meta-actions are applied on objects to trigger a specific rule, they might as well trigger transitions outside of the target action rule scope. Those additional transitions are called side effects, which could be positive or negative. Negative side effects could be devastating in some domains such as healthcare. In this paper, we strive to reduce those negative side effects by extracting personalized action rules. We proposed three object-grouping schemes with regards to same negative side effects to extract personalized action rules for each object group. We also studied the tinnitus handicap inventory data to apply and compare the three grouping schemes.

Keywords

Meta-Actions; Action-Rules; Personalization; Optimization; Grouping

Conflicts of Interest

The authors declare no conflicts of interest.

References

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