Spectral fuzzy classification: an application

Geographical information (including remotely sensed data) is usually imprecise, meaning that the boundaries between different phenomena are fuzzy. In fact, many classes in nature show internal gradual differences in species, health, age, moisture, as well other factors. If our classification model does not acknowledge that those classes are heterogeneous, and crisp classes are artificially imposed, a final careful analysis should always search for the consequences of such an unrealistic assumption. We consider the unsupervised algorithm presented by A. del Amo et al. (2000), and its application to a real image in Sevilla province (south Spain). Results are compared with those obtained from the ERDAS ISO-DATA classification program on the same image, showing the accuracy of our fuzzy approach. As a conclusion, it is pointed out that whenever real classes are natural fuzzy classes, with gradual transition between classes, then its fuzzy representation will be more easily understood, and therefore accepted by users     

An online learning fuzzy controller

A fuzzy controller with online learning capability is reported in this paper. The controller learns from a standard proportional plus derivative (PD) controller. It is implicitly assumed that the tuning parameters of the PD controller are already known. The learning is realized via Wang's table lookup scheme. The controllers are applied successfully to control an open-loop unstable system, i.e., the ball and plate system. Experimental studies have demonstrated the performance of the proposed controller.     

Optimal learning for patterns classification in RBF networks

The proposed modifying of the structure of the radial basis function (RBF) network by introducing the weight matrix to the input layer (in contrast to the direct connection of the input to the hidden layer of a conventional RBF) so that the training space in the RBF network is adaptively separated by the resultant decision boundaries and class regions is reported. The training of this weight matrix is carried out as for a single-layer perceptron together with the clustering process. In this way the network is capable of dealing with complicated problems, which have a high degree of interference in the training data, and achieves a higher classification rate over the current classifiers using RBF     

Partial-distortion-weighted fuzzy competitive learning algorithmfor vector quantisation

An effective competitive learning algorithm called the partial-distortion-weighted fuzzy competitive learning (PDW-FCL) algorithm is developed for vector quantisation. The PDW-FCL algorithm is able to minimise the likelihood of neuron (codevector) underuse and make good use of every neuron for optimal vector quantiser design     

集成学习在短文本分类中的应用研究

为了进一步提高基于深度神经网络短文本分类性能,提出将集成学习方法应用于5种不同的神经网络文本分类器,即卷积神经网络、双向长短时记忆网络、卷积循环神经网络、循环卷积神经网络、分层注意力机制神经网络,分别对两种集成学习方法(Bagging,Stacking)进行了测试。实验结果表明:将多个神经网络短文本分类器进行集成的分类性能要优于单一文本分类模型;进一步两两集成的实验验证了单个模型对短文本分类性能的贡献率。     

Evolutionary learning, reinforcement learning, and fuzzy rules forknowledge acquisition in agent-based systems

The behavior of agents in complex and dynamic environments cannot be programmed a priori, but needs to self-adapt to the specific situations. We present some approaches based on evolutionary reinforcement learning algorithms, which are able to evolve in real-time fuzzy models that control behaviors. We discuss an application where an agent learns how to adapt its behavior to the different behaviors of the other agents it is interacting with, and another application where a group of agents co-evolve cooperative behaviors by using explicit communication to propose the cooperation and to distribute reinforcement to the others     

Estimation of forest parameters through fuzzy classification of TMdata

Several studies have investigated the utility of Landsat 5 TM imagery to estimate forest parameters such as stand composition and density. Regression equations have generally been used to relate these parameters to the radiance responses of the TM channels. Such a method is not feasible in highly complex landscapes, where forest mixtures and terrain irregularities may obscure the existence of simple relationships. A fuzzy approach to the problem is presented based on a multi-step procedure. First, some typical forest plots with known features are spectrally identified. A maximum likelihood fuzzy classification with nonparametric priors is then applied to the study images, so as to derive fuzzy membership grades for all pixels with respect to the typical plots. Finally, these grades are used to compute the estimates of the forest parameters by a weighted average strategy. The method was tested on a complex, rugged area in Tuscany mainly covered by deciduous and coniferous forests. Two TM scenes and accurate ground references taken in spring and summer 1991 were utilized for the testing. The first results, statistically evaluated in comparison with those of a more usual multivariate regression procedure, are quite encouraging. The possible application of the fuzzy approach to other cases of environmental monitoring is finally discussed     

Combined compression and classification with learning vectorquantization

Combined compression and classification problems are becoming increasingly important in many applications with large amounts of sensory data and large sets of classes. These applications range from automatic target recognition (ATR) to medical diagnosis, speech recognition, and fault detection and identification in manufacturing systems. In this paper, we develop and analyze a learning vector quantization (LVQ) based algorithm for combined compression and classification. We show convergence of the algorithm using the ODE method from stochastic approximation. We illustrate the performance of the algorithm with some examples     

Global-guided weakly-supervised learning for multi-label image classification

Multi-label classification with region-free labels is attracting increasing attention compared to that with region-based labels due to the time-consuming manual region-labeling process. Existing methods usually employ attention-based technology to discover the conspicuous label-related regions in a weakly-supervised manner with only image-level region-free labels, while the region covering is not precise without exploring global clues of multi-level features. To address this issue, a novel Global-guided Weakly-Supervised Learning (GWSL) method for multi-label classification is proposed. The GWSL first extracts the multi-level features to estimate their global correlation map which is further utilized to guide feature disentanglement in the proposed Feature Disentanglement and Localization (FDL) networks. Specifically, the FDL networks then adaptively combine the different correlated features and localize the fine-grained features for identifying multiple labels. The proposed method is optimized in an end-to-end manner under weakly supervision with only image-level labels. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts for multi-label learning problems on several publicly available image datasets. To facilitate similar researches in the future, the codes are directly available online at https://github.com/Yong-DAI/GWSL.     

Adaptive hypergraph learning and its application in image classification

Recent years have witnessed a surge of interest in graph-based transductive image classification. Existing simple graph-based transductive learning methods only model the pairwise relationship of images, however, and they are sensitive to the radius parameter used in similarity calculation. Hypergraph learning has been investigated to solve both difficulties. It models the high-order relationship of samples by using a hyperedge to link multiple samples. Nevertheless, the existing hypergraph learning methods face two problems, i.e., how to generate hyperedges and how to handle a large set of hyperedges. This paper proposes an adaptive hypergraph learning method for transductive image classification. In our method, we generate hyperedges by linking images and their nearest neighbors. By varying the size of the neighborhood, we are able to generate a set of hyperedges for each image and its visual neighbors. Our method simultaneously learns the labels of unlabeled images and the weights of hyperedges. In this way, we can automatically modulate the effects of different hyperedges. Thorough empirical studies show the effectiveness of our approach when compared with representative baselines.     

基于增量式学习的圆锥角膜分类算法

圆锥角膜是一种进展性的角膜疾病,多发于青春期,会造成不规则散光以及视力下降,晚期致盲需进行角膜移植,因此圆锥角膜的早期精准筛查是阻止疾病进展避免恶化的必要条件。神经网络作为一种经典的算法是圆锥角膜诊断的有效工具。但随着圆锥角膜病例数据日益增长,为了充分利用新增数据,往往需要对所有样本重新训练,这将耗费大量的时间。为了解决上述问题,本文提出集成神经网络的增量式学习算法,以实现圆锥角膜的智能诊断。此外,本文还引入欠采样和代价敏感思想,用于解决已有增量式学习算法无法处理不均衡数据的问题。实验结果表明,本文提出的算法识别精度达到97%,并且所需训练时间短、存储空间少,因此本算法能够更高效地辅助圆锥角膜诊断。     

A fuzzy set approach for yield learning modeling in wafermanufacturing

The yield of semiconductor manufacturing can be improved through a learning process. A learning model is usually used to describe the learning process and to predict future yields. However, in traditional learning models such as Gruber's general yield model, the uncertainty and variation inherent in the learning process are not easy to consider. Also there are many strict assumptions about parameter distributions that need to be made. These result in the unreliability and imprecision of yield prediction. To improve the reliability and precision of yield prediction, expert opinions are consulted to evaluate and modify the learning model in this study. The fuzzy set theory is applied to facilitate this consulting process. At first, fuzzy forecasts are generated to predict future yields. The necessity of specifying strict parameter distributions is thus relaxed. Fuzzy yield forecasts can be defuzzified, or their α-cuts can be considered in capacity planning. The interpretation of such a treatment is also intuitive. Then, experts are requested to evaluate the learning model and express their opinions about the parameters in suitable fuzzy numbers or linguistic terms defined in advance. Two correction functions are designed to incorporate expert opinions in the learning model. Some examples are used for demonstration. The advantages of the proposed method are then discussed     

基于机器学习和深度学习的网络流量分类研究

随着互联网技术的不断发展以及网络规模的不断扩大,应用的类别纷繁复杂,新型应用层出不穷。为了保障用户服务质量(QoS)并确保网络安全,准确快速的流量分类是运营商及网络管理者亟须解决的问题。首先给出网络流量分类的问题定义和性能指标;然后分别介绍基于机器学习和基于深度学习的流量分类方法,分析了这些方法的优缺点,并对现存问题进行阐述;接着围绕流量分类线上部署时会遇到的3个问题:数据集问题、新应用识别问题、部署开销问题对相关工作进行阐述与分析,并进一步探讨目前网络流量分类研究面临的挑战;最后对网络流量分类下一步的研究方向进行展望。     

Extracting fuzzy rules from polysomnographic recordings for infant sleep classification

A neuro-fuzzy classifier (NFC) of sleep-wake states and stages has been developed for healthy infants of ages 6 mo and onward. The NFC takes five input patterns previously identified on 20-s epochs from polysomnographic recordings and assigns them to one out of five possible classes: Wakefulness, REM-Sleep, Non-REM Sleep Stage 1, Stage 2, and Stage 3-4. The definite criterion for a sleep state or stage to be established is duration of at least 1 min. The data set consisted of a total of 14 continuous recordings of naturally occurring naps (average duration: 143 +/- 39 min), corresponding to a total of 6021 epochs. They were divided in a training, a validation and a test set with 7, 2, and 5 recordings, respectively. During supervised training, the system determined the fuzzy concepts associated to the inputs and the rules required for performing the classification, extracting knowledge from the training set, and pruning nonrelevant rules. Results on an independent test set achieved 83.9 +/- 0.4% of expert agreement. The fuzzy rules obtained from the training examples without a priori information showed a high level of coincidence with the crisp rules stated by the experts, which are based on internationally accepted criteria. These results show that the NFC can be a valuable tool for implementing an automated sleep-wake classification system.     

基于模糊集分类的单幅图像去雾算法

针对高亮度区域导致大气光强度A 计算不准确以及复原图像颜色失真影响图像去 雾效果的问题,提出一种基于模糊集分类的单幅图像去雾算法。首先从暗通道模型出发,对 图像进行分割并采用基于模糊集理论的图像分 类算法确定符合暗通道先验理论的非明亮区域,避免了天空等高亮区域对大气光强度计算 的影响;然后利用快速双边滤波方法既具有平滑效果,又具有边缘细节保持的特性,估计大 气耗 散函数,进而精确恢复场景透射率;最后由大气散射模型复原图像,并进行基于人眼视觉的 亮 度、色调的调整,修正图像中颜色失真区域,提高视觉效果。与经典算法相比,本文算法在 细节、色彩保真度具有较大改进。     

Multiple kernel-based multi-instance learning algorithm for image classification

In this paper, a novel multi-instance learning (MIL) algorithm based on multiple-kernels (MK) framework has been proposed for image classification. This newly developed algorithm defines each image as a bag, and the low-level visual features extracted from its segmented regions as instances. This algorithm is started from constructing a “word-space” from instances based on a collection of “visual-words” generated by affinity propagation (AP) clustering method. After calculating the distance between a “visual-word” and the bag (image), a nonlinear mapping mechanism is introduced for registering each bag as a coordinate point in the “word-space”. In this case, the MIL problem is transformed into a standard supervised learning problem, which allows multiple-kernels support vector machine (MKSVM) classifiers to be trained for the image categorization. Compared with many popular MIL algorithms, the proposed method, named as MKSVM-MIL, shows its satisfactorily experimental results on the COREL dataset, which highlights the robustness and effectiveness for image classification applications.     

Studying cost-sensitive learning for multi-class imbalance in Internet traffic classification

Cost-sensitive learning has been applied to resolve the multi-class imbalance problem in Internet traffic classification and it has achieved considerable results.But the classification performance on the minority classes with a few bytes is still unhopeful because the existing research only focuses on the classes with a large amount of bytes.Therefore,the class-dependent misclassification cost is studied.Firstly,the flow rate based cost matrix(FCM) is investigated.Secondly,a new cost matrix named weighted cost matrix(WCM) is proposed,which calculates a reasonable weight for each cost of FCM by regarding the data imbalance degree and classification accuracy of each class.It is able to further improve the classification performance on the difficult minority class(the class with more flows but worse classification accuracy).Experimental results on twelve real traffic datasets show that FCM and WCM obtain more than 92% flow g-mean and 80% byte g-mean on average;on the test set collected one year later,WCM outperforms FCM in terms of stability.     

Research on ECG classification based on transfer learning

Concerning current deep learning-based electrocardiograph(ECG) classification methods, there exists domain discrepancy between the data distributions of the training set and the test set in the inter-patient paradigm. To reduce the negative effect of domain discrepancy on the classification accuracy of ECG signals, this paper incorporates transfer learning into the ECG classification, which aims at applying the knowledge learned from the training set to the test set. Specifically, this paper fir...     

基于机器学习的多光谱模糊图像降噪方法

利用当前方法对多光谱模糊图像降噪时,未对多光谱模糊图像进行增强处理,存在图像视觉效果差、主观分数低等问题。为此,提出基于机器学习的多光谱模糊图像降噪方法。首先,利用均值滤波模板增强多光谱模糊图像色彩,同时利用高斯模板增强图像细节,将两者叠加,保证图像不受失真和光晕现象等影响,保证图像以及边界的清晰度;然后,利用核主成分分析法构建图像去噪模型,将图像坐标全部投射到特征空间中;最后,采用机器学习去噪特征空间中的近似噪点,实现多光谱模糊图像降噪。实验结果表明,所提方法的图像视觉效果较好,且主观得分较高。