Learning from noisy data using a non-covering ILP algorithm

Authors:
Andrej Oblak;Ivan Bratko
Affiliations:
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Venue:
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Year:
2010

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Abstract

In this paper we describe the non-covering inductive logic programming program HYPER/N, concentrating mainly on noise handling as well as some other mechanisms that improve learning. We perform some experiments with HYPER/N on synthetic weather data with artificially added noise, and on real weather data to learn to predict the movement of rain from radar rain images and synoptic data.