Fuzzy repertory table: a method for acquiring knowledge about input variables to machine learning algorithm

• Published in: IEEE transactions on fuzzy systems Vol. 12; no. 1; pp. 123 - 139
• Main Authors: Castro-Schez, J.J., Castro, J.L., Zurita, J.M.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-02-2004; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Expert systems; Fuzzy; Fuzzy logic; Fuzzy set theory; Fuzzy systems; Input variables; Knowledge acquisition; Knowledge based systems; Knowledge engineering; Learning; Machine learning; Machine learning algorithms; Mathematical analysis; Psychology; Studies; Tables (data); Training data
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2003.822684

In this paper, we develop a technique for acquiring the finite set of attributes or variables which the expert uses in a classification problem for characterising and discriminating a set of elements. This set will constitute the schema of a training data set to which an inductive learning algorithm will be applied. The technique developed uses ideas taken from psychology, in particular from Kelly's Personal Construct Theory. While we agree that Kelly's repertory grid technique is an efficient way to do this, it has several disadvantages which we shall try to solve by using a fuzzy repertory table. With the suggested technique, we aim to obtain the set of attributes and values which the expert can use to "measure" the object type (class) on the classification problem in some way. We will also acquire some general rules to identify the expert's evident knowledge; these rules will comprise concepts belonging to their conceptual structure.