John S. Chandler

Integrating statistical and inductive learning methods for knowledge acquisition

Abstract Inductive learning is a method for automated knowledge acquisition. It converts a set of training data into a knowledge structure. In the process of knowledge induction, statistical techniques can play a major role in improving performance. In this paper, we investigate the competition and integration between the traditional statistical and the inductive learning methods. First, the competition between these two approaches is examined. Then, a general framework for integrating these two approaches is presented. This framework suggests three possible integrations: (1) statistical methods as preprocessors for inductive learning, (2) inductive learning methods as preprocessors for statistical classification, and (3) the combination of the two methods to develop new algorithms. Finally, empirical evidence concerning these three possible integrations are discussed. The general conclusion is that algorithms integrating statistical and inductive learning concepts are likely to make the most improvement in performance.

The impact of measurement scale and correlation structure on classification performance of inductive learning and statistical methods

Abstract This is a comparative study of inductive learning and statistical methods using the simulation approach to provide a generalizable results. The purpose of this study is to investigate the impact of measurement scale of explanatory variables on the relative performance of the statistical method (probit) and the inductive learning method (ID3) and to examine the impact of correlation structure on the classification behavior of the probit method and the ID3 method. The simulation results show that the relative classification accuracy of ID3 to probit increases as the proportion of binary variables increases in the classification model, and that the relative accuracy of ID3 to probit is higher when the covariance matrices are unequal among populations than when the covariance matrices are equal among populations. The empirical tests on ID3 reveal that the classification accuracy of ID3 is lower when the covariance matrices are unequal among populations than when the covariance matrices are equal among populations and that the classification accuracy of ID3 decreases as the correlations among explanatory variables increases.

A course on the management of the systems development process with hands-on computing

Abstract A course on the development of accounting information systems from a managerial perspective that employed hands-on computing experience is described. Central to the course is a group term project that involves the design and implementation of computer-based accounting applications for actual businesses. The applications are developed in a microcomputer environment. A chronology of the course is described identifying the assignments, successes and problems of the course. The results of a student survey are also presented and analyzed. The lessons learned from the experience, for both student and instructor, and suggestions for improvements conclude this discussion.