In this paper, a new knowledge characteristics weighting method based on the rough set and knowledge granulation theory is proposed. They presented a weighting method based on the conditional information entropy and rough set, but that method also involved additional costs. Zhu and Chen constructed the priority queue of characteristics importance to improve Bao’s research. But in this method the characteristics importance obtained by redundant characteristics was higher than that got by nonredundant characteristics. It avoids some nonredundant characteristics to be handled by redundant characteristics. proposed a method ascertaining characteristics weight based on rough set and conditional information entropy. Therefore, some nonredundant characteristics would be handled by redundant characteristics. This method achieved the unity of the subjective a priori knowledge with the objective situations, but it ignored the internal difference in the equivalent partitions. Cao and Liang combined the characteristics importance of the rough set and the experts’ a priori knowledge to determine the characteristics weight. However, this method did not consider the influence of decision characteristics on conditional characteristics. proposed a method to determine the characteristics weights. For instance, based on the concepts of characteristics importance, Wang et al. In recent years, the rough set method has been studied to calculate the characteristics weight. Now, the rough set theory has been widely used in pattern recognition, data mining, machine learning, and other fields. Rough set theory can be used to analyze and process the fuzzy or uncertain data without the a priori knowledge. It has become an extremely useful tool to handle the imprecision and uncertainty knowledge. Rough set theory was firstly proposed by Pawlak in 1982. In these methods, the a priori knowledge must be used. The common weighting methods include experts scoring method, fuzzy statistics method, Analytic Hierarchy Process (AHP) method, and Principal Component Analysis (PCA) method. Weights reflect the role of characteristics in the classification process and directly affect the validity and accuracy of the classifier. Therefore, it is very important to compute the weights of characteristics sets. In data mining, in order to effectively classify the knowledge, we need to make proper assessment on the knowledge characteristics sets. Experimental results on several UCI data sets demonstrate that the weighting method can effectively avoid subjective arbitrariness and avoid taking the nonredundant characteristics as redundant characteristics. In this paper, a new method based on rough set and knowledge granulation theories is proposed to ascertain the characteristics weight. Too much redundancy might cause inaccuracy, and less redundancy might cause ineffectiveness. However, the current rough set weighting methods could not obtain a balanced redundant characteristic set. Most of the existing characteristics weighting methods always rely heavily on the experts’ a priori knowledge, while rough set weighting method does not rely on experts’ a priori knowledge and can meet the need of objectivity. The knowledge characteristics weighting plays an extremely important role in effectively and accurately classifying knowledge.
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