Affiliations: Informatics Dept., University of South Carolina
Upstate Spartanburg, SC, USA. E-mail: ATzacheva@uscupstate.edu | Dept. of Electronics, Computer, & Information
Technology School of Technology, North Carolina A&T State University,
Greensboro, NC, USA. E-mails: ltsay@ncat.edu & lishiangtsay@yahoo.com
Note: [] Address for correspondence: Dept. of Electronics, Computer,
& Information Technology, School of Technology, North Carolina A&T
State University, Greensboro, NC, USA
Abstract: A rule is actionable, if a user can do an action to his/her
advantage based on that rule. Actionability can be expressed in terms of
attributes that are present in a database. Action rules are constructed from
certain pairs of classification rules, previously extracted from the same
database, each defining a preferable decision class. It is assumed that
attributes are divided into two groups: stable and flexible. Flexible
attributes provide a tool for making hints to a user to what changes within
some values of flexible attributes are needed to re-classify a group of
objects, supporting the action rule, from one decision class to another, more
desirable, one. Changes of values of some flexible attributes can be more
expensive than changes of other values. To investigate such cases, the notion
of a cost is introduced and it is assigned by an expert to each such a change.
Action rules construction involves both flexible and stable
attributes listed in certain pairs of classification rules. The values of
stable attributes are used to create action forest. We propose a new strategy which combines the action forest algorithm
of extracting action rules and a heuristic strategy for generating
reclassification rules of the lowest cost. This new strategy presents an
enhancement to both methods.
