Fuzzy c-means based support vector machines classifier for perfume recognition

Identification of more than three perfumes is very difficult for the human nose. It is also a problem to recognize patterns of perfume odor with an electronic nose that has multiple sensors. For this reason, a new hybrid classifier has been presented to identify type of perfume from a closely similar data set of 20 different odors of perfumes. The structure of this hybrid technique is the combination of unsupervised fuzzy clustering c-mean (FCM) and supervised support vector machine (SVM). On the other hand this proposed soft computing technique was compared with the other well-known learning algorithms. The results show that the proposed hybrid algorithm’s accuracy is 97.5% better than the others.     

Mahalanobis distance based on fuzzy clustering algorithm for image segmentation

Conventional Fuzzy C-means (FCM) algorithm uses Euclidean distance to describe the dissimilarity between data and cluster prototypes. Since the Euclidean distance based dissimilarity measure only characterizes the mean information of a cluster, it is sensitive to noise and cluster divergence. In this paper, we propose a novel fuzzy clustering algorithm for image segmentation, in which the Mahalanobis distance is utilized to define the dissimilarity measure. We add a new regularization term to the objective function of the proposed algorithm, reflecting the covariance of the cluster. We experimentally demonstrate the effectiveness of the proposed algorithm on a generated 2D dataset and a subset of Berkeley benchmark images.     

Various approaches to speed up Mahalanobis distance classifier

In this paper, we propose various approaches to reduce the CPU time requirement of Mahalanobis distance classifier (MDC). Based on the results discussed in this paper, we have found that the Ensemble average approach and Quadratic form range theorem are superior to other algorithms. The algorithms based on these two approaches are found to give a maximum speed of 7 and 9 with MSS and TM data, respectively.     

Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization,Mahalanobis distance and post-segmentation correction

In this paper, we propose an improvement method for image segmentation using the fuzzy c-means clustering algorithm (FCM). This algorithm is widely experimented in the field of image segmentation with very successful results. In this work, we suggest further improving these results by acting at three different levels. The first is related to the fuzzy c-means algorithm itself by improving the initialization step using a metaheuristic optimization. The second level is concerned with the integration of the spatial gray-level information of the image in the clustering segmentation process and the use of Mahalanobis distance to reduce the influence of the geometrical shape of the different classes. The final level corresponds to refining the segmentation results by correcting the errors of clustering by reallocating the potentially misclassified pixels. The proposed method, named improved spatial fuzzy c-means IFCMS, was evaluated on several test images including both synthetic images and simulated brain MRI images from the McConnell Brain Imaging Center (BrainWeb) database. This method is compared to the most used FCM-based algorithms of the literature. The results demonstrate the efficiency of the ideas presented.     

Fuzzy fault diagnosis based on fuzzy robust v-support vector classifier and modified genetic algorithm

This paper presents a new version of fuzzy support vector classifier machine (SVM) which can penalize those hybrid noises to forecast fuzzy nonlinear system. Since there exist some problems of uncertain data in many actual forecasting problem, the input variables are described as fuzzy numbers by fuzzy comprehensive evaluation. To solve the shortage of ε-insensitive loss function for hybrid noises such as singularity points, biggish magnitude noises and Gaussian noises, a novel robust loss function is proposed in this paper. Then by the integration of the triangular fuzzy theory, v-SVC and robust loss function theory, fuzzy robust v-SVC (FRv-SVM) which can penalize those hybrid noises is proposed. To seek the optimal parameters of FRv-SVC, genetic algorithm is also proposed to optimize the unknown parameters of FRv-SVC. The results of the application in fuzzy car assembly line system diagnosis confirm the feasibility and the validity of the FRv-SVC model. Compared with other SVC methods, FRv-SVC method has better classifier precison for small sample with hybrid noises.     

基于减法聚类改进的模糊c-均值算法的模糊聚类研究

针对模糊c-均值(FCM)聚类算法受初始聚类中心影响,易陷入局部最优,以及算法对孤立点数据敏感的问题,提出了解决方案:采用快速减法聚类算法初始化聚类中心,为每个样本点赋予一个定量的权值,用来区分不同的样本点对最终的聚类结果的不同作用,为提高聚类速度采用修正隶属度矩阵的方法,并将算法与传统的FCM相比.实验结果表明,该算法较好地解决了初值问题,与随机初始化方法相比,迭代次数少、收敛速度快、具有较好的聚类结果.     

An integrated approach to fuzzy learning vector quantization andfuzzy c-means clustering

Derives an interpretation for a family of competitive learning algorithms and investigates their relationship to fuzzy c-means and fuzzy learning vector quantization. These algorithms map a set of feature vectors into a set of prototypes associated with a competitive network that performs unsupervised learning. Derivation of the new algorithms is accomplished by minimizing an average generalized distance between the feature vectors and prototypes using gradient descent. A close relationship between the resulting algorithms and fuzzy c-means is revealed by investigating the functionals involved. It is also shown that the fuzzy c-means and fuzzy learning vector quantization algorithms are related to the proposed algorithms if the learning rate at each iteration is selected to satisfy a certain condition     

基于支持向量机的粗糙分类器

支持向量机和粗糙集理论是两种分类技术.前者寻求最大化两类间隔的最优分类超平面,后者用逻辑规则解释分类.基于两者的关系,提出了一种复合算法,且将其推广到回归.新算法在一定程度降低了计算复杂度,且适用于软间隔分类.数值实验表明新算法是有效可行的.     

基于Mahalanobis距离的运动意识分类研究

提出基于Mahalanobis距离判别式算法的意识任务分类方法.对被测试者想象左右手运动时脑电信号的mu节律能量变化进行在线动态分析,提取EEG(C3,C4)两个通道的mu节律能量作为特征向量,用Mahalanobis距离判别式算法对左右手运动想象脑电模式进行分类,实验结果表明,正确识别率可达87.86%.     

Car assembly line fault diagnosis based on triangular fuzzy support vector classifier machine and particle swarm optimization

This paper presents a new version of fuzzy support vector classifier machine to diagnose the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of finite samples and uncertain data in complex fuzzy fault system modeling, the input and output variables are described as fuzzy numbers. Then by integrating the fuzzy theory and v-support vector classifier machine, the triangular fuzzy v-support vector regression machine (TF v-SVCM) is proposed. To seek the optimal parameters of TF v-SVCM, particle swarm optimization (PSO) is also applied to optimize parameters of TF v-SVCM. A diagnosing method based on TF v-SVCM and PSO are put forward. The results of the application in fault system diagnosis confirm the feasibility and the validity of the diagnosing method. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on TF v-SVCM and PSO is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than standard v-SVCM.     

基于加权模糊C均值聚类的空预器积灰监测研究

电站空预器积灰会严重影响机组运行经济性.提出加权模糊C均值聚类算法对空预器积灰程度进行监测,该方法计算多维样本中每一维数据的标准差,将其作为权重,计算样本与类心之间的加权欧式距离,降低模糊C均值聚类算法对离群点的敏感度.利用人工数据对该方法进行验证,结果表明,相比于传统模糊C均值聚类算法,提出的方法对离群点识别更加准确...     

基于马氏距离判别法的企业资信评估

针对基于传统统计学的企业资信评估方法的不足,提出基于距离判别法的企业资信评估方法,并用实例通过Matlab软件及其相应工具对其进行了计算判别.结果表明,基于距离判别法的企业资信评估方案,可快捷、准确、有效地评价企业是否处于破产状态,为企业资信评估提供可靠的依据.     

Unsupervised hyperspectral feature selection based on fuzzy c-means and grey wolf optimizer

Hyperspectral image (HSI) with hundreds of narrow and consecutive spectral bands provides substantial information to discriminate various land-covers. However, the existence of redundant features/bands not only gives rise to increasing of computation time but also interferes the classification result of hyperspectral images. Obviously, it is a very challenging problem how to select an effective feature subset from original bands to reduce the dimensionality of the hyperspectral dataset. In this study, a novel unsupervised feature selection method is suggested to remove the redundant features of HSI by feature subspace decomposition and optimization of feature combination. Feature subset decomposition is achieved by the fuzzy c-means (FCM) algorithm. The optimal feature selection is based on the optimization process of grey wolf optimizer (GWO) algorithm and maximum entropy (ME) principle. To evaluate the effectiveness of the proposed method, experiments are conducted on three well-known hyperspectral datasets, Indian Pines, Pavia University, and Salinas. Six state-of-the-art feature selection methods are used to compare with the proposed method. Experimental results successfully confirm the superior performance of our proposal with respect to three classification accuracy indices overall accuracy (OA), average accuracy (AA) and kappa coefficient (κ).     

A parsimony fuzzy rule-based classifier using axiomatic fuzzy set theory and support vector machines

This paper proposes a classification method that is based on easily interpretable fuzzy rules and fully capitalizes on the two key technologies, namely pruning the outliers in the training data by SVMs (support vector machines), i.e., eliminating the influence of outliers on the learning process; finding a fuzzy set with sound linguistic interpretation to describe each class based on AFS (axiomatic fuzzy set) theory. Compared with other fuzzy rule-based methods, the proposed models are usually more compact and easily understandable for the users since each class is described by much fewer rules. The proposed method also comes with two other advantages, namely, each rule obtained from the proposed algorithm is simply a conjunction of some linguistic terms, there are no parameters that are required to be tuned. The proposed classification method is compared with the previously published fuzzy rule-based classifiers by testing them on 16 UCI data sets. The results show that the fuzzy rule-based classifier presented in this paper, offers a compact, understandable and accurate classification scheme. A balance is achieved between the interpretability and the accuracy.     

Classification based on fuzzy robust PCA algorithms and similarity classifier

In this article, classification method is proposed where data is first preprocessed using fuzzy robust principal component analysis (FRPCA) algorithms to obtain data in a more feasible form. After this we use similarity classifier for the classification. We tested this procedure for breast cancer data and liver-disorder data. The results were quite promising and better classification accuracy was achieved than using traditional PCA and similarity classifier. Fuzzy robust principal component analysis algorithms seem to have the effect that they project these data sets in a more feasible form, and together with similarity classifier classification on accuracy of 70.25% was achieved with liver-disorder data and 98.19% accuracy was achieved with breast cancer data. Compared to the results achieved with traditional PCA and similarity classifier about 4% higher accuracy was achieved with liver-disorder data and about 0.5% higher accuracy was achieved with breast cancer data.     

Car assembly line fault diagnosis based on triangular fuzzy Gaussian support vector classifier machine and modified genetic algorithm

This paper presents a new version of fuzzy support vector classifier machine to diagnose the nonlinear fuzzy fault system with multi-dimensional input variables. Since there exist problems of Gaussian noises and uncertain data in complex fuzzy fault system modeling, the input and output variables are described as fuzzy numbers. Then by integrating fuzzy theory, Gaussian loss function and v-support vector classifier machine, the fuzzy Gaussian v-support vector regression machine (Fg-SVCM) is proposed. To seek the optimal parameters of Fg-SVCM, the modified genetic algorithm (GA) is also applied to optimize parameters of Fg-SVCM. A diagnosing method based on Fg-SVCM and GA is put forward. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on Fg-SVCM and PSO is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than other v-SVCMs.     

Type-2 fuzzy logic-based classifier fusion for support vector machines

As a machine-learning tool, support vector machines (SVMs) have been gaining popularity due to their promising performance. However, the generalization abilities of SVMs often rely on whether the selected kernel functions are suitable for real classification data. To lessen the sensitivity of different kernels in SVMs classification and improve SVMs generalization ability, this paper proposes a fuzzy fusion model to combine multiple SVMs classifiers. To better handle uncertainties existing in real classification data and in the membership functions (MFs) in the traditional type-1 fuzzy logic system (FLS), we apply interval type-2 fuzzy sets to construct a type-2 SVMs fusion FLS. This type-2 fusion architecture takes considerations of the classification results from individual SVMs classifiers and generates the combined classification decision as the output. Besides the distances of data examples to SVMs hyperplanes, the type-2 fuzzy SVMs fusion system also considers the accuracy information of individual SVMs. Our experiments show that the type-2 based SVM fusion classifiers outperform individual SVM classifiers in most cases. The experiments also show that the type-2 fuzzy logic-based SVMs fusion model is better than the type-1 based SVM fusion model in general.     

基于支持向量机和输出编码的文本分类器研究

介绍了一种支持向量机与输出编码相结合的文本分类器算法 ,采用一对多、一对一和纠错编码三种编码方式以及相似度计算的海明码距、边界损失方法进行文本分类和测试 ,表明一对多编码与边界损失相似度计算相结合的分类器系统具有最高的查全率和查准率。     

Rough set based generalized fuzzy c-means algorithm and quantitative indices.

A generalized hybrid unsupervised learning algorithm, which is termed as rough-fuzzy possibilistic c-means (RFPCM), is proposed in this paper. It comprises a judicious integration of the principles of rough and fuzzy sets. While the concept of lower and upper approximations of rough sets deals with uncertainty, vagueness, and incompleteness in class definition, the membership function of fuzzy sets enables efficient handling of overlapping partitions. It incorporates both probabilistic and possibilistic memberships simultaneously to avoid the problems of noise sensitivity of fuzzy c-means and the coincident clusters of PCM. The concept of crisp lower bound and fuzzy boundary of a class, which is introduced in the RFPCM, enables efficient selection of cluster prototypes. The algorithm is generalized in the sense that all existing variants of c-means algorithms can be derived from the proposed algorithm as a special case. Several quantitative indices are introduced based on rough sets for the evaluation of performance of the proposed c-means algorithm. The effectiveness of the algorithm, along with a comparison with other algorithms, has been demonstrated both qualitatively and quantitatively on a set of real-life data sets.