ABSTRACT
In reality, due to various reasons, empirical data often has the property of granularity and may be incomplete, imprecise, or even conflicting. For example, in diagnosing a manufacturing system, the opinions of two engineers can be different, or even contradictory. Some earlier inductive learning systems such as the once prevailing decision tree learning system, the ID3, are unable to deal with imprecise and inconsistent information present in empirical training data [Khoo et al., 1999]. Thus, the ability to handle imprecise and inconsistent information has become one of the most important requirements for a classification system.
