Previous [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Journal of Inforamtion Science and Engineering, Vol.17 No.1, pp.85-94 (January 2001)

A Genetics-Based Approach to Knowledge Integration and Refinement

Ching-Hung Wang, Tzung-Pei Hong*, and Shian-Shyong Tseng+
Chunghwa Telecommunication Laboratories
Chungli, Taiwan 326, R.O.C.
E-mail: amidofu@cht.com.tw
*Department of Electrical Engineering
National University of Kaohsiung
Kaohsiung, Taiwan 840, R.O.C.
E-mail: tphong@nuk.edu.tw
+Institute of Computer and Information Science
National Chiao Tung University
Hsinchu, Taiwan 300, R.O.C.
E-mail: sstseng@cis.nctu.edu.tw

In this paper, we propose a genetics-based knowledge integration approach to integrate multiple rule sets into a central rule set. The proposed approach consists of two phases: knowledge encoding and knowledge integrating. In the encoding phase, each knowledge input is translated and expressed as a rule set, and then encoded as a bit string. The combined bit strings form an initial knowledge population, which is then ready for integrating. In the knowledge integration phase, a genetic algorithm generates an optimal or nearly optimal rule set from these initial knowledge inputs. Furthermore, a rule-refinement scheme is proposed to refine inference rules via interaction with the environment. Experiments on diagnosing brain tumors were carried out to compare the accuracy of a rule set generated by the proposed approach with that of initial rule sets derived from different groups of experts or induced by means of various machine learning techniques. Results show that the rule set derived using the proposed approach is much more accurate than each initial rule set on its own.

Keywords: brain tumor, expert system, genetic algorithm, knowledge integration, knowledge refinement

Full Text () Retrieve PDF document (200101_05.pdf : 3,563,874 bytes)

Received May 7, 1998; revised August 5, 1998; accepted October 9, 1998.
Communicated by Jieh Hsiang.