A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation.

Zhenquan Shi,Shiping Chen

doi:10.1155/2018/1838639

Abstract

The knowledge characteristics weighting plays an extremely important role in effectively and accurately classifying knowledge. 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. However, the current rough set weighting methods could not obtain a balanced redundant characteristic set. Too much redundancy might cause inaccuracy, and less redundancy might cause ineffectiveness. In this paper, a new method based on rough set and knowledge granulation theories is proposed to ascertain the characteristics weight. 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.

Highlights

In data mining, in order to effectively classify the knowledge, we need to make proper assessment on the knowledge characteristics sets
Some experiments are used to show the effectiveness of our new method
The method based on the dependence of rough set The method based on rough set and conditional information entropy The method of this paper c1 0.5000 0.5000 0.3625

Summary

Introduction

In order to effectively classify the knowledge, we need to make proper assessment on the knowledge characteristics sets. The common weighting methods include experts scoring method, fuzzy statistics method [1,2,3], Analytic Hierarchy Process (AHP) method [4,5,6], and Principal Component Analysis (PCA) method [7, 8]. In these methods, the a priori knowledge must be used. Rough set theory can be used to analyze and process the fuzzy or uncertain data without the a priori knowledge [11,12,13,14,15,16,17]. The rough set theory has been widely used in pattern recognition [18,19,20], data mining [21,22,23], machine learning [24,25,26,27,28,29], and other fields [30,31,32,33,34,35,36]

Methods

Results

Conclusion