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.     

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F-SVMs的多模型建模方法

针对全局模型难以精确描述复杂工业过程的问题,提出一种基于模糊支持向量机(F-SVMs)的多模型(F-SVMs MM)建模方法。用模糊支持向量分类算法(F-SVC)对输入数据进行预处理,得到多模型模糊隶属度;用模糊支持回归算法(F-SVR)建立多模型(MM)估计器。应用该方法对pH中和滴定过程进行建模,仿真结果表明,F-SVMs MM跟踪性能好、泛化能力强,比USOCPN方法和标准支持向量机(SVMs)方法具有更好的性能和推广能力。     

支持向量机-模糊推理自学习控制器设计

常规的模糊推理系统大多由专家经验建立模糊规则,自学习能力不强.提出了一种支持向量机-模糊推理系统,由支持向量机实现模糊推理系统的自学习,并设计了一种支持向量机-模糊推理自学习控制器.文章给出了自学习控制器的结构和学习算法,对比研究了变尺度梯度优化和混沌优化两种学习算法.针对非线性对象的仿真实验验证了该控制器的优良性能,控制效果比模糊逻辑控制器更好.     

一类直接构造的模糊多类支持向量分类器

针对直接多类分类方法,提出了一种新的基于直接构造多类SVM分类器的模糊多类支持向量机算法FCS-SVM。在算法中,重构了优化问题及其约束条件,以及Lagrange公式,并进行了推导。通过在标准数据集上的几个实验,对这些算法进行了比较分析。实验结果表明提出的算法可以得到比较理想的分类精度。     

Classification model for product form design using fuzzy support vector machines

Consumer preferences regarding product design are often affected by a large variety of form features. Traditionally, the quality of product form design depended heavily on designers’ intuitions and did not always prove to be successful in the marketplace. In this study, to help product designers develop appealing products in a more effective manner, an approach based on fuzzy support vector machines (fuzzy SVM) is proposed. This constructs a classification model of product form design based on consumer preferences. The one-versus-one (OVO) method is adopted to handle a multiclass problem by breaking it into various two-class problems. Product samples were collected and their form features were systematically examined. To formulate a classification problem, each product sample was assigned a class label and a fuzzy membership that corresponded to this label. The OVO fuzzy SVM model was constructed using collected product samples. The optimal training parameter set for the model was determined by a two-step cross-validation. A case study of mobile phone design is given to demonstrate the effectiveness of the proposed methodology. The performance of fuzzy SVM is also compared with SVM. The results of the experiment show that fuzzy SVM performed better than SVM.     

决策树支持向量机多分类器设计的向量投影法

针对如何有效地设计决策树支持向量机(SVM)多类分类器的层次结构这个关键问题,提出一种基于向量投影的类间可分性测度的设计方法,并给出一种基于该类间可分性测度设计决策树SVM多分类器层次结构的方法.为加快每个SVM子分类器的训练速度且保持其高推广性,将基于向量投影的支持向量预选取方法用于每个子分类器的训练中.通过对3个大规模数据集和手写体数字识别的仿真实验表明,新方法能有效地提高决策树SVM多类分类器的分类精度和速度.     

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.     

On-line fuzzy modeling via clustering and support vector machines

In this paper, we propose a novel approach to identify unknown nonlinear systems with fuzzy rules and support vector machines. Our approach consists of four steps which are on-line clustering, structure identification, parameter identification and local model combination. The collected data are firstly clustered into several groups through an on-line clustering technique, then structure identification is performed on each group using support vector machines such that the fuzzy rules are automatically generated with the support vectors. Time-varying learning rates are applied to update the membership functions of the fuzzy rules. The modeling errors are proven to be robustly stable with bounded uncertainties by a Lyapunov method and an input-to-state stability technique. Comparisons with other related works are made through a real application of crude oil blending process. The results demonstrate that our approach has good accuracy, and this method is suitable for on-line fuzzy modeling.     

Using robust dispersion estimation in support vector machines

In this paper, a novel Support Vector Machine (SVM) variant, which makes use of robust statistics, is proposed. We investigate the use of statistically robust location and dispersion estimators, in order to enhance the performance of SVMs and test it in two-class and multi-class classification problems. Moreover, we propose a novel method for class specific multi-class SVM, which makes use of the covariance matrix of only one class, i.e., the class that we are interested in separating from the others, while ignoring the dispersion of other classes. We performed experiments in artificial data, as well as in many real world publicly available databases used for classification. The proposed approach performs better than other SVM variants, especially in cases where the training data contain outliers. Finally, we applied the proposed method for facial expression recognition in three well known facial expression databases, showing that it outperforms previously published attempts.     

Goal distance estimation for automated planning using neural networks and support vector machines

Many of today’s most successful planners perform a forward heuristic search. The accuracy of the heuristic estimates and the cost of their computation determine the performance of the planner. Thanks to the efforts of researchers in the area of heuristic search planning, modern algorithms are able to generate high-quality estimates. In this paper we propose to learn heuristic functions using artificial neural networks and support vector machines. This approach can be used to learn standalone heuristic functions but also to improve standard planning heuristics. One of the most famous and successful variants for heuristic search planning is used by the Fast-Forward (FF) planner. We analyze the performance of standalone learned heuristics based on nature-inspired machine learning techniques and employ a comparison to the standard FF heuristic and other heuristic learning approaches. In the conducted experiments artificial neural networks and support vector machines were able to produce standalone heuristics of superior accuracy. Also, the resulting heuristics are computationally much more performant than related ones.     

基于模糊支持向量机与决策树的文本分类器

针对模糊支持向量机在文本分类应用中的隶属度函数确定问题,提出了一种基于模糊支持向量机与决策树的文本分类器的构建方法。该方法不仅考虑了样本与类中心之间的关系,还根据传统支持向量机中包含支持向量且平行于分类面的平面构建切球,来确定类中各个样本之间的关系,由样本点与球的位置关系计算其隶属度,可以合理地区分有效样本和噪音、孤立点样本。并与决策树方法相结合,实现多类分类。实验结果表明,该方法具有良好的分类效果。     

Fusion of remotely sensed data for soil moisture estimation using relevance vector and support vector machines

A data assimilation (DA) methodology that uses two state-of-the-art techniques, relevance vector machines (RVMs) and support vector machines (SVMs), is applied to retrieve surface (0–6 cm) soil moisture content (SMC) and SMC at a depth of 30 cm. RVMs and SVMs are known for their robustness, efficiency and sparseness and provide a statistically sound approach to solve inverse problems and thus to build statistical models. Here, we build a statistical model that produces acceptable estimations of SMC by using inexpensive and readily available data. The study area for this research is the Walnut Creek watershed in Ames, south-central Iowa, USA. The data were obtained from Soil Moisture Experiments 2002 (SMEX02) conducted at Ames, Iowa. The DA methodology combines remotely sensed inputs with field measurements, crop physiological characteristics, soil temperature, soil water-holding capacity and meteorological data to build a two-step model to estimate SMC using both techniques, i.e. RVMs and SVMs. First, the RVM is used to build a model that retrieves surface (0–6 cm) SMC. This information serves as a boundary condition for the second step of this model, which estimates SMC at a depth of 30 cm. An exactly similar routine is followed with an SVM for estimation of surface (0–6 cm) SMC and SMC at a depth of 30 cm. The results from the RVM and SVM models are compared and statistics show that RVMs perform better (root mean square error (RMSE)?= 0.014 m³ m^?3) when compared with SVMs (RMSE?= 0.017 m³ m^?3) with a reduced computational complexity and more suitable real-time implementation. Cross-validation techniques are used to optimize the model. Bootstrapping is used to check over/under-fitting and uncertainty in model estimates. Computations show good agreement with the actual SMC measurements with coefficients of determination (R ²) for RVM equal to 0.92 and for SVM equal to 0.88. Statistics indicate a good model generalization capability with indexes of agreement (IoAs) for RVM equal to 0.97 and for SVM equal to 0.96.     

一种新的模糊支持向量机多分类算法

在模糊多分类问题中,由于训练样本在训练过程中所起的作用不同,对所有数据包括异常数据赋予一个隶属度。针对模糊支持向量机（fuzzy support vector machines,FSVM）的第一种形式,引入类中心的概念,结合一对多1-a-a(one-against-all)组合分类方法,提出了一种基于一对多组合的模糊支持向量机多分类算法,并与1-a-1(one-against-one)组合和1-a-a组合的分类算法比较。数值实验表明,该算法是有效的,有较高的分类准确率,有更好的泛化能力。     

Developing fast predictors for large-scale time series using fuzzy granular support vector machines

With the widespread application of computer and communication technologies, more and more real-time systems are implemented whose large amounts of time-stamped data consequently require more efficient processing approaches. For large-scale time series, precise values are often hard or even impossible to predict in limited time at limited costs. Meanwhile, precision is not absolutely necessary for human to think and reason, so credible changing ranges of time series are satisfactory for some decision-making problems. This study aims to develop fast interval predictors for large-scale, nonlinear time series with noisy data using fuzzy granular support vector machines (FGSVMs). Six information granulation methods are proposed which can granulate large-scale time series into subseries. FGSVM predictors are developed to forecast credible changing ranges of large-scale time series. Five performance indicators are presented to measure the quality and efficiency of FGSVMs. Four time series are used to examine the effectiveness and efficiency of the proposed granulation methods and the developed FGSVMs, whose results show the effectiveness and advantages of FGSVMs for large-scale, nonlinear time series with noisy data.     

级联SVM和分类器融合的人脸性别识别方法

传统的支持向量机（Support Vector Machines,SVM）在面对大样本训练问题时,其样本数量会受到内存的限制。因此,提出一种基于级联SVM和分类器融合的人脸图像性别识别方法。级联SVM分类器可以通过设定阈值将识别难易程度不同的样本分成若干层次来进行训练;同时,在级联的每一层上,为了降低分类器在识别过程中受各种因素的影响,对不同特征维数下得到的最优分类器进行融合,通过融合减小误差,使中性的人脸样本有更明确的分类。在同一硬件条件下的实验结果表明,单层SVM最多只能训练7万样本,而四层级联SVM训练样本数可达12万以上,相应的识别率也从单层融合前的96.7%上升至四层融合后的99.1%。     

Financial decision support using neural networks and support vector machines

Abstract: Bankruptcy prediction and credit scoring are the two important problems facing financial decision support. The multilayer perceptron (MLP) network has shown its applicability to these problems and its performance is usually superior to those of other traditional statistical models. Support vector machines (SVMs) are the core machine learning techniques and have been used to compare with MLP as the benchmark. However, the performance of SVMs is not fully understood in the literature because an insufficient number of data sets is considered and different kernel functions are used to train the SVMs. In this paper, four public data sets are used. In particular, three different sizes of training and testing data in each of the four data sets are considered (i.e. 3:7, 1:1 and 7:3) in order to examine and fully understand the performance of SVMs. For SVM model construction, the linear, radial basis function and polynomial kernel functions are used to construct the SVMs. Using MLP as the benchmark, the SVM classifier only performs better in one of the four data sets. On the other hand, the prediction results of the MLP and SVM classifiers are not significantly different for the three different sizes of training and testing data.     

Human motion recognition using support vector machines

We propose a motion recognition strategy that represents each videoclip by a set of filtered images, each of which corresponds to a frame. Using a filtered-image classifier based on support vector machines, we classify a videoclip by applying majority voting over the predicted labels of its filtered images and, for online classification, we identify the most likely type of action at any moment by applying majority voting over the predicted labels of the filtered images within a sliding window. We also define a classification confidence and the associated threshold in both cases, which enable us to identify the existence of an unknown type of motion and, together with the proposed recognition strategy, make it possible to build a real-time motion recognition system that cannot only make classifications in real-time, but also learn new types of motions and recognize them in the future. The proposed strategy is demonstrated on real datasets.     

Texture classification using the support vector machines

In recent years, support vector machines (SVMs) have demonstrated excellent performance in a variety of pattern recognition problems. In this paper, we apply SVMs for texture classification, using translation-invariant features generated from the discrete wavelet frame transform. To alleviate the problem of selecting the right kernel parameter in the SVM, we use a fusion scheme based on multiple SVMs, each with a different setting of the kernel parameter. Compared to the traditional Bayes classifier and the learning vector quantization algorithm, SVMs, and, in particular, the fused output from multiple SVMs, produce more accurate classification results on the Brodatz texture album.     

Patent value analysis using support vector machines

Receiving patents or licenses is an inevitable act of research in order to protect new ideas leading innovation. Request for patents has increased exponentially in order to legalize the intellectual property. Measuring economical value of each patent has been widely studied in the literature. Majority of the research in this field is focused on the patent driver prospect handled for the patent offices. There are a variety of criteria affecting decisions on each patent right; and predicting the possibility of grant may help the researchers to take some precautions. Objective of this study is to propose a robust model to determine if the appeal has a chance of approval. A case study is run on the patents that are accepted and rejected in home appliance industry to construct an intelligent classification model. The support vector machine, Back-Propagation Network and Bayes classification methods are compared on the proposed model. The proposed model in this study will help the decision makers to predict whether the patent appeal will be accepted. The study is unique with the approach that helps the candidate patent owners.