Reasoning with unknown, not-applicable and irrelevant meta-values in concept learning and pattern discovery

摘要: This paper describes methods for reasoning with unknown, irrelevant, and not-applicable meta-values when learning concept descriptions from examples or discovering patterns in data. These types of represent different reasons which regular values are not available, thus require treatment both rule testing. The presented handled internally, within the testing algorithms, preprocessing as those widely described literature. They have been implemented AQ21 multitask knowledge discovery program, experimentally tested on three real world one designed datasets.