ON THE CHOICE OF THE BEST TEST FOR ATTRIBUTE DEPENDENCY IN PROGRAMS FOR LEARNING FROM EXAMPLES
Jerzy W. Grzymala‐Busse, Sachin Mithal · International Journal of Software Engineering and Knowledge Engineering · 1991
The paper discusses a problem associated with learning from examples. Learning programs under consideration, LEM and LERS, were designed to automate knowledge acquisition for expert systems. Hence, both programs induce rules in the minimal discriminant form, i.e., rules based on minimal sets of relevant attributes, called coverings. The problem addressed in the paper is the selection of the best algorithm for determining coverings. Four different methods, based on indiscernibility relation, partition, characteristic set and lower boundary are compared. Both theoretical analysis and experimental results of multiple running of many sets of examples, with variable number of examples and with variable number of attributes are taken into account. As a result the partition method is determined to be the most efficient way to compute coverings.