Based on the stability and inequality of texture features between coal and rock,this study used the digital image analysis technique to propose a coal–rock interface detection method.By using gray level co-occurrence...Based on the stability and inequality of texture features between coal and rock,this study used the digital image analysis technique to propose a coal–rock interface detection method.By using gray level co-occurrence matrix,twenty-two texture features were extracted from the images of coal and rock.Data dimension of the feature space reduced to four by feature selection,which was according to a separability criterion based on inter-class mean difference and within-class scatter.The experimental results show that the optimized features were effective in improving the separability of the samples and reducing the time complexity of the algorithm.In the optimized low-dimensional feature space,the coal–rock classifer was set up using the fsher discriminant method.Using the 10-fold cross-validation technique,the performance of the classifer was evaluated,and an average recognition rate of 94.12%was obtained.The results of comparative experiments show that the identifcation performance of the proposed method was superior to the texture description method based on gray histogram and gradient histogram.展开更多
互信息过滤式特征选择算法往往仅局限于互信息这一度量标准.为规避采取单一的互信息标准的局限性,在互信息的基础上引入基于距离度量的算法RReliefF,从而得出更好的过滤式准则.将RReliefF用于分类任务,度量特征与标签的相关性;应用最大...互信息过滤式特征选择算法往往仅局限于互信息这一度量标准.为规避采取单一的互信息标准的局限性,在互信息的基础上引入基于距离度量的算法RReliefF,从而得出更好的过滤式准则.将RReliefF用于分类任务,度量特征与标签的相关性;应用最大互信息系数(maximal information coefficient,MIC)度量特征与特征之间的冗余性、特征与标签的相关性;最后,应用熵权法为MIC和RReliefF进行客观赋权,提出了基于熵权法的过滤式特征选择算法(filtering feature selection algorithm based on entropy weight method,FFSBEWM).在13个数据集上进行对比实验,结果表明,FFSBEWM所选择的特征子集的平均分类准确率和最高分类准确率均优于其他对比算法.展开更多
针对传统支持向量机(Support Vector Machine,SVM)集成学习(Ensemble Learning,EL)方法不能够解决高维复杂数据且子学习器差异性小集成效果不明显的问题,提出一种基于多种特征选择方法进行Bagging集成的支持向量机学习(Support Vector M...针对传统支持向量机(Support Vector Machine,SVM)集成学习(Ensemble Learning,EL)方法不能够解决高维复杂数据且子学习器差异性小集成效果不明显的问题,提出一种基于多种特征选择方法进行Bagging集成的支持向量机学习(Support Vector M achine Based on M ultiple Feature Selection Bagging,M FSB_SVM)方法.该方法首先采用不同的特征选择方法构建子学习器,以增加不同子学习器间的差异性,并直接从训练数据中对样本特征的重要性进行评估,而无需学习算法的反馈.实验表明,本文提出的MFSB_SVM方法既可以有效解决高维数据问题,也可避免传统SVM集成方法效果不明显的缺点,从而进一步提高学习模型的泛化性能.展开更多
基金the National Natural Science Foundation of China(No.51134024/E0422)for the financial support
文摘Based on the stability and inequality of texture features between coal and rock,this study used the digital image analysis technique to propose a coal–rock interface detection method.By using gray level co-occurrence matrix,twenty-two texture features were extracted from the images of coal and rock.Data dimension of the feature space reduced to four by feature selection,which was according to a separability criterion based on inter-class mean difference and within-class scatter.The experimental results show that the optimized features were effective in improving the separability of the samples and reducing the time complexity of the algorithm.In the optimized low-dimensional feature space,the coal–rock classifer was set up using the fsher discriminant method.Using the 10-fold cross-validation technique,the performance of the classifer was evaluated,and an average recognition rate of 94.12%was obtained.The results of comparative experiments show that the identifcation performance of the proposed method was superior to the texture description method based on gray histogram and gradient histogram.
文摘互信息过滤式特征选择算法往往仅局限于互信息这一度量标准.为规避采取单一的互信息标准的局限性,在互信息的基础上引入基于距离度量的算法RReliefF,从而得出更好的过滤式准则.将RReliefF用于分类任务,度量特征与标签的相关性;应用最大互信息系数(maximal information coefficient,MIC)度量特征与特征之间的冗余性、特征与标签的相关性;最后,应用熵权法为MIC和RReliefF进行客观赋权,提出了基于熵权法的过滤式特征选择算法(filtering feature selection algorithm based on entropy weight method,FFSBEWM).在13个数据集上进行对比实验,结果表明,FFSBEWM所选择的特征子集的平均分类准确率和最高分类准确率均优于其他对比算法.
文摘针对传统支持向量机(Support Vector Machine,SVM)集成学习(Ensemble Learning,EL)方法不能够解决高维复杂数据且子学习器差异性小集成效果不明显的问题,提出一种基于多种特征选择方法进行Bagging集成的支持向量机学习(Support Vector M achine Based on M ultiple Feature Selection Bagging,M FSB_SVM)方法.该方法首先采用不同的特征选择方法构建子学习器,以增加不同子学习器间的差异性,并直接从训练数据中对样本特征的重要性进行评估,而无需学习算法的反馈.实验表明,本文提出的MFSB_SVM方法既可以有效解决高维数据问题,也可避免传统SVM集成方法效果不明显的缺点,从而进一步提高学习模型的泛化性能.
