Inexact field learning: an approach to induce high quality rules from low quality data

To avoid low quality problems caused by low quality data, the paper introduces an inexact field learning approach which derives rules by working on the fields of attributes with respect to classes, rather than on individual point values of attributes. The experimental results show that field learning achieved a higher prediction accuracy rate on new unseen test cases which is particularly true when the learning is performed on large low quality data.