Generalizing Data in Natural Language

This paper concerns the development of a new direction in machine learning, called natural induction, which requires from computergenerated knowledge not only to have high predictive accuracy, but also to be in human-oriented forms, such as natural language descriptions and/or graphical representations. Such forms facilitate understanding and acceptance of the learned knowledge, and making mental models that are useful for decision making. An initial version of the AQ21-NI program for natural induction and its several novel features are briefly described. The performance of the program is illustrated by an example of deriving medical diagnostic rules from micro-array data.

• Type: Conference
• Year: 2007
• Where: RSEISP
• Authors: Ryszard S. Michalski, Janusz Wojtusiak
• Keywords: Control Systems, Forms Facilitate Understanding, Natural Induction, Natural Language Descriptions, RSEISP 2007