Knowledge reduction is an important issue in data mining. This paper focuses on the problem of attribute reduction in incomplete decision tables. Three types of incomplete conditional entropy are introduced based on tolerance relation, such as H′conditional entropy, E′conditional entropy, and I′conditional entropy, which are proved to be an extension of the concept of conditional entropy in incomplete decision tables. Compared with H′and I′conditional entropy, E′ conditional entropy decreases monotonously with the amount of attributes. Based onE′conditional entropy, a new reduced definition is presented, which integrates the complete and incomplete information systems into the corresponding reduced algorithm. Finally, the experimental result shows that this algorithm can find the reduct of decision tables.