Improved KNN-SMOreg algorithm and its application in predicting the amount of hematite from uranium

Q3 Engineering
Jia Wu, Z. Cai, Zhechao Gao
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引用次数: 0

Abstract

Hematite,as a typical alteration mineral,plays a very important role in uranium exploration.Traditional modeling method usually treats every feature with the same probability.However,this does not hold in many real world applications,which may also cause the reduction of the accuracy of prediction.We propose a novel method called WKNN-SMOreg,which weights the features according to the association of their attributes on the hybrid of KNN and SMOreg.In this way,the error caused by the features with lower association will be reduced.The experiment results show,compared with KNN,SVM and KNN-SMOreg,the novel method improves the accuracy of prediction,and reduces the negative impact of the noise,which also implies that the new method can be well applied in the prediction of alteration minerals.
改进KNN-SMOreg算法及其在铀中赤铁矿量预测中的应用
赤铁矿作为一种典型的蚀变矿物,在铀矿找矿中起着十分重要的作用。传统的建模方法通常以相同的概率对待每一个特征。然而,这在许多现实世界的应用中并不成立,这也可能导致预测准确性的降低。我们提出了一种新的方法WKNN-SMOreg,该方法在KNN和SMOreg混合的基础上,根据特征的属性关联对特征进行加权。这样可以减少关联度较低的特征所带来的误差。实验结果表明,与KNN、SVM和KNN- smoreg相比,该方法提高了预测精度,减小了噪声的负面影响,可以很好地应用于蚀变矿物预测。
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来源期刊
应用基础与工程科学学报
应用基础与工程科学学报 Engineering-Engineering (all)
CiteScore
1.60
自引率
0.00%
发文量
2784
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