Affiliations: School of Management and Engineering, Nanjing
University, Nanjing, P.R. China. E-mail: huaxiongli@gmail.com; zhouxz@nju.edu.cn | Department of Computer Science, University of Regina,
Regina, Canada. E-mail: yyao@cs.uregina.ca | School of Information Science, Nanjing Audit
University, Nanjing, P.R. China. E-mail: hbhuangbing@126.com
Abstract: A two-phase learning strategy for rule induction from incomplete
data is proposed, and a new form of rules is introduced so that a user can
easily identify attributes with or without missing values in a rule. Two levels
of measurement are assigned to a rule. An algorithm for two-phase rule
induction is presented. Instead of filling in missing attribute values before
or during the process of rule induction, we divide rule induction into two
phases. In the first phase, rules and partial rules are induced based on
non-missing values. In the second phase, partial rules are modified and refined
by the imputation of some missing values. Such rules truthfully reflect the
knowledge embedded in the incomplete data. The study not only presents a new
view of rule induction from incomplete data, but also provides a practical
solution. Experiments validate the effectiveness of the proposed method.
