Decision trees for symbolic knowledge based on contingency table analysis

Thomas W. Rauber; A. S. Steiger-Garcao

doi:10.1117/12.150599

1 September 1993 Decision trees for symbolic knowledge based on contingency table analysis

Thomas W. Rauber, A. S. Steiger-Garcao

Proceedings Volume 1962, Adaptive and Learning Systems II; (1993) https://doi.org/10.1117/12.150599
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

In this paper we point out an alternative basis for splitting a node of a decision tree. We use exactly the same framework of the tree generation as ID3 does, in order to be able to compare the results properly. The splitting of the sample set is also done locally at a tree node, without considering earlier decisions about the partition of the samples. Only one attribute is used to split the samples. We point out different splitting criteria. Contingency tables are a technique in nonparametric statistics to analyze categorical (symbolic) populations. Among other useful applications of contingency tables, dependence tests between rows and columns of the table can be performed. A sample set is inserted into a contingency table with classes as columns and all values of an attribute as rows. A variety of measurements of dependence can then be derived. Results in respect to the two most important qualities of decision trees, the error rate and tree complexity, are presented. For a set of selected benchmark examples the performance of ID3 and the contingency table approach are compared. It is shown that in many cases the contingency table method exhibits lower estimated error rates or has less nodes for the generated decision tree.

Citation Download Citation

Thomas W. Rauber and A. S. Steiger-Garcao "Decision trees for symbolic knowledge based on contingency table analysis", Proc. SPIE 1962, Adaptive and Learning Systems II, (1 September 1993); https://doi.org/10.1117/12.150599

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available