本文对基于粗集的数据预处理中数据补齐和连续属性离散化问题进行讨论。
This thesis discusses the question of data reinforce and continuous feature discretization which is based upon data preprocessing of rough set.
利用实例仿真验证表明，LS-SVM具有较好的泛化能力和很强的鲁棒性，采用基于LS-SVM的交通流时间序列模型补齐丢失数据能够取得很好的效果。
The model for filling time series data of traffic flow based on LS-SVM is proposed in this paper, missing data can be filled by using traffic flow historical data.
利用实例仿真验证表明，LS-SVM具有较好的泛化能力和很强的鲁棒性，采用基于LS-SVM的交通流时间序列模型补齐丢失数据能够取得很好的效果。
The model for filling time series data of traffic flow based on LS-SVM is proposed in this paper, missing data can be filled by using traffic flow historical data.
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