Learning compound decision functions for sequential data in dialog with experts

Authors:
Wojciech Jaworski
Affiliations:
Faculty of Mathematics, Computer Science and Mechanics, Warsaw University, Warsaw, Poland
Venue:
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Year:
2006

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Abstract

In this paper, we investigate the problem of learning the decision functions for sequential data describing complex objects that are composed of subobjects. The decision function maps sequence of attribute values into a relational structure, representing properties of the object described by the sequence. This relational structure is constructed in a way that allows us to answer questions from a given language. The decision function is constructed by means of rule system. The rules are learned incrementally in a dialog with an expert. We also present an algorithm that implements the rule system and we apply it to real life problems.