Learning inductive rules using hellinger measure
Chang‐Hwan Lee · Applied Artificial Intelligence · 1999
Systems for inducing classification rules from databases are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents an information theoretic approach for extracting knowledge from databases in the form of inductive rules using Hellinger measure, an entropy function which is utilized as a criteria for selecting rules generated from databases. In order to reduce the complexity of rule generation, the characteristics of Hellinger measure are analyzed and used to prune the search space of hypothesis. The system is implemented and tested on some well-known machine-learning databases.