Using decision trees and feature construction to describe changing consumer lifestyles and expectations
Raymond L. Major, Gary J. Kœhler · University of Florida Digital Collections (University of Florida) · 1994
Using artificial intelligence methods to acquire expert knowledge inductively is a key area of interest in Expert-Systems Development. This dissertation investigates the theoretical properties of feature-construction learning algorithms and uses them to develop an empirical model to examine several issues related to the 1990-1991 recession. Empirical results show that feature construction can improve the performance of an induced decision tree. We develop an analytical model of learning with feature construction. Our model characterizes the time complexity of learning boolean functions with polynomial size DNF expressions, when bounded-rank decision trees are used as a concept description language. Results show that limiting the number of new features may improve the computational efficiency of feature construction. Our procedure uses the dual of a decision tree when forming new features. We then use our empirical model to (1) describe changes in consumer life-styles and expectations for time periods associated with the 1990-1991 recession, and, (2) show that current practice for creating quantitative measures of consumer confidence is sometimes inappropriately used. Finally, we examine tradeoffs between expert comprehensibility and formal power, when choosing a representation to use in expert-system applications.