基于粗糙集和知识粒度的特征权重确定方法

A NEW METHOD OF KNOWLEDGE CHARACTERISTICS WEIGHTING BASED ON ROUGH SET AND KNOWLEDGE GRANULATION

摘要:

知识特征对于认识、分辨知识具有重要的作用。知识特征权重的确定在对知识进行有效准确的分类中起着非常重要的作用。当前的权重确定主要依赖于相关专家已有的经验。粗糙集特征权重的确定可以摆脱专家先验知识,从而达到权重客观性的要求。原有的粗糙集特征权重的确定方法中存在要么特征冗余无法得到权重,要么为了克服冗余而得到不能真正体现实际情况的权重。本文在粗糙集理论的基础上运用知识粒度理论计算特征权重。通过算例证明,本文提出的方法可以解决权重确定的主观随意性和特征冗余等问题,提高了权重确定方法的适用性。

Abstract:

The knowledge characteristics weighting plays an extremely important role in the effective and accurate knowledge classification. The present characteristics weighting methods always rely heavily on the experts’ priori knowledge. Rough set weighting method can get rid of priori knowledge so as to reflect the objectivity of the data. For the current rough set weighting methods, they could neither help obtain the weight because of the redundant characteristics, nor could the obtained weight reflect the objective situations. In this paper, a new method based on rough set and knowledge granulation theories was proposed to ascertain the characteristics weight. Experimental results on several UCI data sets demonstrate the effectiveness of this new weighting method, such as its subjective randomness, applicable and explicable.