The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors.The brain tumor is characterized by an anomalous proliferation of ...The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors.The brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or malignant.Most tumors are misdiagnosed due to the variabil-ity and complexity of lesions,which reduces the survival rate in patients.Diagno-sis of brain tumors via computer vision algorithms is a challenging task.Segmentation and classification of brain tumors are currently one of the most essential surgical and pharmaceutical procedures.Traditional brain tumor identi-fication techniques require manual segmentation or handcrafted feature extraction that is error-prone and time-consuming.Hence the proposed research work is mainly focused on medical image processing,which takes Magnetic Resonance Imaging(MRI)images as input and performs preprocessing,segmentation,fea-ture extraction,feature selection,similarity measurement,and classification steps for identifying brain tumors.Initially,the medianfilter is practically applied to the input image to reduce the noise.The graph-cut segmentation technique is used to segment the tumor region.The texture feature is extracted from the output of the segmented image.The extracted feature is selected by using the Ant Colony Opti-mization(ACO)algorithm to improve the performance of the classifier.This prob-abilistic approach is used to solve computing issues.The Euclidean distance is used to calculate the degree of similarity for each extracted feature.The selected feature value is given to the Relevance Vector Machine(RVM)which is a multi-class classification technique.Finally,the tumor is classified as abnormal or nor-mal.The experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87%when compared to the traditional Support Vector Machine(SVM)technique.展开更多
Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version...Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.展开更多
In the scenario of underwater acoustic sparse channel estimation with training sequences,grid points in the measuring matrix are caused by discretizing procedure.Estimated accuracy might not be guaranteed with the sta...In the scenario of underwater acoustic sparse channel estimation with training sequences,grid points in the measuring matrix are caused by discretizing procedure.Estimated accuracy might not be guaranteed with the state-of-the-art methods when multipath delays don't exactly locate on the grid points.In this paper,we construct a gridless measuring matrix for sparse channel estimation which contains an off-grid adjusting factor.The Relevance Vector Machine(RVM) algorithm is employed to estimate this factor.The numerical experiments for two different underwater channels are performed to testify the newly proposed method.The results demonstrate that this method outperforms conventional ones in terms of estimating error and bit error rate,especially when the grid gets coarser.展开更多
A new intelligent method for disease diagnosis based on rough set theory （RST） and the relevance vector machine （RVM） for classification is presented as the rough relevance vector machine （RRVM）. The RRVM mixes ...A new intelligent method for disease diagnosis based on rough set theory （RST） and the relevance vector machine （RVM） for classification is presented as the rough relevance vector machine （RRVM）. The RRVM mixes rough set＇s strong rule extraction ability with the excellent classification ability of the relevance vector machine through preprocessing initial information, reducing data, and training the relevance vector machine. Compared with traditional intelligence methods such as neural network（NN）, support vector machine（SVM）, and relevance vector machine （RVM）, this method manages to identify disease samples objectively and effectively with less transcendental information.展开更多
针对具有多维状态变量、多种工作模式和故障模式的复杂工程系统,提出一种基于综合健康指数(synthesized health index,SHI)与相关向量机(relevance vector machine,RVM)的系统级失效预测方法。在离线训练阶段,先根据有限失效历史数据建...针对具有多维状态变量、多种工作模式和故障模式的复杂工程系统,提出一种基于综合健康指数(synthesized health index,SHI)与相关向量机(relevance vector machine,RVM)的系统级失效预测方法。在离线训练阶段,先根据有限失效历史数据建立各工作模式下的健康评估模型,并据此获得各历史退化轨迹的SHI序列;然后再使用RVM对这些序列进行回归处理,进而辨识出与回归曲线最为匹配的函数模型。在线预测阶段,先运用健康评估模型计算当前设备的SHI序列并进行RVM回归,再拟合出离线阶段确定的函数模型并添加时变噪声;最后,外推预测出系统剩余使用寿命的概率密度分布。该方法成功应用到涡轮发动机的失效预测案例。展开更多
文摘The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors.The brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or malignant.Most tumors are misdiagnosed due to the variabil-ity and complexity of lesions,which reduces the survival rate in patients.Diagno-sis of brain tumors via computer vision algorithms is a challenging task.Segmentation and classification of brain tumors are currently one of the most essential surgical and pharmaceutical procedures.Traditional brain tumor identi-fication techniques require manual segmentation or handcrafted feature extraction that is error-prone and time-consuming.Hence the proposed research work is mainly focused on medical image processing,which takes Magnetic Resonance Imaging(MRI)images as input and performs preprocessing,segmentation,fea-ture extraction,feature selection,similarity measurement,and classification steps for identifying brain tumors.Initially,the medianfilter is practically applied to the input image to reduce the noise.The graph-cut segmentation technique is used to segment the tumor region.The texture feature is extracted from the output of the segmented image.The extracted feature is selected by using the Ant Colony Opti-mization(ACO)algorithm to improve the performance of the classifier.This prob-abilistic approach is used to solve computing issues.The Euclidean distance is used to calculate the degree of similarity for each extracted feature.The selected feature value is given to the Relevance Vector Machine(RVM)which is a multi-class classification technique.Finally,the tumor is classified as abnormal or nor-mal.The experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87%when compared to the traditional Support Vector Machine(SVM)technique.
基金Supported by the National Natural Science Foundation of China (No.41001285)
文摘Geospatial objects detection within complex environment is a challenging problem in remote sensing area. In this paper, we derive an extension of the Relevance Vector Machine (RVM) technique to multiple kernel version. The proposed method learns an optimal kernel combination and the associated classifier simultaneously. Two feature types are extracted from images, forming basis kernels. Then these basis kernels are weighted combined and resulted the composite kernel exploits interesting points and appearance information of objects simultaneously. Weights and the detection model are finally learnt by a new algorithm. Experimental results show that the proposed method improve detection accuracy to above 88%, yields good interpretation for the selected subset of features and appears sparser than traditional single-kernel RVMs.
基金supported by the National Natural Science Foundation of China(61431020)
文摘In the scenario of underwater acoustic sparse channel estimation with training sequences,grid points in the measuring matrix are caused by discretizing procedure.Estimated accuracy might not be guaranteed with the state-of-the-art methods when multipath delays don't exactly locate on the grid points.In this paper,we construct a gridless measuring matrix for sparse channel estimation which contains an off-grid adjusting factor.The Relevance Vector Machine(RVM) algorithm is employed to estimate this factor.The numerical experiments for two different underwater channels are performed to testify the newly proposed method.The results demonstrate that this method outperforms conventional ones in terms of estimating error and bit error rate,especially when the grid gets coarser.
基金Supported by the National Natural Science Foundation of China (70771708)
文摘A new intelligent method for disease diagnosis based on rough set theory （RST） and the relevance vector machine （RVM） for classification is presented as the rough relevance vector machine （RRVM）. The RRVM mixes rough set＇s strong rule extraction ability with the excellent classification ability of the relevance vector machine through preprocessing initial information, reducing data, and training the relevance vector machine. Compared with traditional intelligence methods such as neural network（NN）, support vector machine（SVM）, and relevance vector machine （RVM）, this method manages to identify disease samples objectively and effectively with less transcendental information.
文摘针对具有多维状态变量、多种工作模式和故障模式的复杂工程系统,提出一种基于综合健康指数(synthesized health index,SHI)与相关向量机(relevance vector machine,RVM)的系统级失效预测方法。在离线训练阶段,先根据有限失效历史数据建立各工作模式下的健康评估模型,并据此获得各历史退化轨迹的SHI序列;然后再使用RVM对这些序列进行回归处理,进而辨识出与回归曲线最为匹配的函数模型。在线预测阶段,先运用健康评估模型计算当前设备的SHI序列并进行RVM回归,再拟合出离线阶段确定的函数模型并添加时变噪声;最后,外推预测出系统剩余使用寿命的概率密度分布。该方法成功应用到涡轮发动机的失效预测案例。
