基于粗糙集和决策树法的认知无线电知识挖掘

Knowledge discovery for cognitive radio based on rough set and decision tree method

摘要:

对粗糙集、决策树C4.5算法进行了研究，提出用粗糙集和决策树相结合的方法设计CR知识挖掘模型，并通过案例研究其可行性；利用基于MATLAB 802.11a物理层仿真平台收集的数据作为CR感知样值，通过样本值训练决策树序列，构建决策树提取知识，并用混淆矩阵法对设计模型的准确性及性能进行评价。实验结果表明，该方法设计模型的分类准确率高，增强了知识的可解释性，能够初步达到认知无线电知识挖掘和对以往经验学习的目的。

Abstract:

It is one of the key issues that making knowledge discovery effectively in a Cognitive Radio(CR) engine design. Basing on the research about Rough Set Theory and C4.5 algorithm of decision tree, this study presented a model of CR knowledge discovery designed by combination of rough set and decision methods and studied its feasibility through a case. Using data based on simulation platform of MATLAB 802.11a physical layer as CR perception sample, decision tree sequence was trained, and decision tree was built for knowledge extraction. Then the accuracy and performance of the design model was evaluated by confusion matrix. The simulation results show that the proposed design model gets high classification accuracy rate, can enhance the interpretability of knowledge，and therefore has preliminarily achieved the purpose of knowledge discovery for cognitive radio and learning from the experiences.