用于不平衡数据分类的0阶TSK型模糊系统

处理不平衡数据分类时,传统模糊系统对少数类样本识别率较低.针对这一问题,首先,在前件参数学习上,提出了竞争贝叶斯模糊聚类（Bayesian fuzzy clustering based on competitive learning,BFCCL）算法,BFCCL算法考虑不同类别样本聚类中心间的排斥作用,采用交替迭代的执行方式并通过马尔科夫蒙特卡洛方法获得模型参数最优解.其次,在后件参数学习上,基于大间隔的策略并通过参数调节使得少数类到分类面的距离大于多数类到分类面的距离,该方法能有效纠正分类面的偏移.基于上述思想以0阶TSK型模糊系统为具体研究对象构造了适用于不平衡数据分类问题的0阶TSK型模糊系统（0-TSK-IDC）.人工和真实医学数据集实验结果表明,0-TSK-IDC在不平衡数据分类问题中对少数类和多数类均具有较高的识别率,且具有良好的鲁棒性和可解释性.     

基于多种群量子进化的区间二型模糊规则挖掘算法

利用智能优化算法挖掘模糊分类规则能够解决模糊前件参数和无关项的组合优化问题,但也存在依赖初始规则以及更新过程无指导等缺陷,导致分类精度难以保证.为此,本文以二型模糊规则分类系统为框架,采用模糊聚类得到代表性样本并启发式的产生初始规则,以量子等位基因形式对规则进行编码生成多初始种群,根据基因的优良性,以变尺度变异操作实现等位基因的指导性进化.在此基础上,利用矛盾规则重构机制,提高模糊规则分类系统的精度.将所提出算法与FH–GBML–IVFS–Amp算法和GAGRAD算法进行了分类精度对比,并在不同噪声水平下,与C4.5算法、朴素贝叶斯分类器和BP神经网络进行分类鲁棒性比较,实验结果表明所提出算法具有较好分类精度与鲁棒性.     

基于模糊区域分布的分类规则提取及推理算法

基于不同分类的样本在各规则对应模糊区域的隶属度分布,定义了一种规则相对匹配度,比分类匹配度更能体现样本在不同模糊区域的分布对比.设计了模糊区域分布矩阵,由该矩阵可以算得规则相对匹配度和分类匹配度,并提出了基于规则相对匹配度的分类规则提取算法,同分类匹配度算法相比,该算法充分考虑了每条规则之间的隶属度分布对比,同时以各分类样本的相对数量作为加权系数,从而兼顾了学习空间的全局密度优势和局部数量优势.通过解模糊器实现了基于规则的分类推理,其推理过程比以往算法具有更好的解释性和简洁性.最后,由Iris数据和Wine数据的分类实验证明:无论样本数量均衡与否,由规则相对匹配度提取规则都具有更好的分类效果.     

区间二型模糊子空间0阶TSK系统

人们倾向于使用少量的有代表性的特征来描述一条规则,而忽略极为次要的冗余的信息。经典的区间二型TSK(Takagi-Sugeno-Kang)模糊系统,在规则前件和后件部分会使用完整的数据特征空间,对于高维数据而言,易导致系统的复杂度增加和可解释性的损失。针对于此,提出了区间二型模糊子空间0阶TSK系统。在规则前件部分,使用模糊子空间聚类和网格划分相结合的方法生成稀疏的规整的规则中心,在规则后件部分,使用简化的0阶形式,从而得到规则语义更为简洁的区间二型模糊系统。在模拟和真实数据上的实验结果表明该方法分类效果良好,可解释性更好。     

基于模糊子空间聚类的0 阶岭回归TSK 模糊系统

经典数据驱动型TSK模糊系统在利用高维数据训练模型时,由于规则前件采用的特征过多,导致规则的解释性和简洁性下降.对此,根据模糊子空间聚类算法的子空间特性,为TSK模型添加特征抽取机制,并进一步利用岭回归实现后件的学习,提出一种基于模糊子空间聚类的0阶岭回归TSK模型构建方法.该方法不仅能为规则抽取出重要子空间特征,而且可为不同规则抽取不同的特征.在模拟和真实数据集上的实验结果验证了所提出方法的优势.     

一种基于精简的模糊规则库的分类算法*

结合模糊聚类和粗糙集提出了一种基于精简的模糊规则库分类算法.对于数值型样本数据,首先采用模糊聚类生成模糊规则库,然后运用粗糙集理论对样本属性进行约简,删除冗余规则,即可得到精简的模糊规则库,以方便进行分类决策.通过对IRIS的仿真测试表明,本算法所产生的模糊规则不仅简单易懂,而且分类效果很好.     

无标记训练样本的Web文本分类方法

在文本分类中获得有类别标记训练样本的代价是很高昂的，本文针对这个问题对传统的模糊聚类方法进行改进，提出模糊划分聚类方法FPCM，将聚类的无监督性和样本的先验知识结合起来，通过相似度度量聚类相关文本，取得比较客观的簇和少量标记文本，为监督学习找到分类依据，并结合朴素贝叶斯增量学习方式进行分类器的学习.本文进一步用估计分类误差损失的方法平衡选取候选样本，提高了分类准确率，实现了应用范围更加广泛的无标记文本分类学习模型.     

适合大规模数据集且基于LLM的0阶TSK模糊分类器

针对传统分类器的泛化性能差、可解释性及学习效率低等问题, 提出0阶TSK-FC模糊分类器.为了将该分类器 应用到大规模数据的分类中, 提出增量式0阶TSK-IFC模糊分类器, 采用增量式模糊聚类算 法(IFCM($c+p$))训练模糊规则参数并通过适当的矩阵变换提升参数学习效率.仿真实验表明, 与FCPM-IRLS模糊分类器、径向基函数神经网 络相比, 所提出的模糊分类器在不同规模数据集中均能保持很好的性能, 且TSK-IFC模糊分类器在大规模数据分类中尤为突出.     

基于改进的PCM支持向量描述多类分类器

基于支持向量数据描述和改进的可能性c-均值聚类算法,提出了一种模糊的多类分类学习机.首先通过一个改进的PCM算法来计算每个样本对于每类的权值矩阵,该权值也反映了该样本对某类的重要程度;然后将该权值矩阵应用到支持向量数据描述方法中,并对样本进行训练;最后给出了一个针对多类分类的分类规则(函数),并从理论上证明该分类规则满足贝叶斯优化决策理论.通过对比实验分析,本文提出的算法在分类精度和训练时间上都有较大的改善.     

一种新的模糊规则提取方法

提出了一种新的模糊规则提取方法,该方法先采用基于山峰函数的减法聚类法自适应地确定初始的聚类中心,然后由此构造动态自组织神经网络进行学习,在学习的过程中可根据情况适当地合并或分裂神经元,并重构神经网络继续学习,最后按聚类中心确定模糊子集数目和隶属函数并形成模糊规则集.实验结果表明,通过网络结构和神经元的动态自适应变化能够获取样本集中的模糊信息,形成直观的模糊规则.     

Iterative Bayesian fuzzy clustering toward flexible icon-based assistive software for the disabled

A novel fuzzy clustering technique, called iterative Bayesian fuzzy clustering (IBFC), is presented and applied for grouping and recommendation of icons associated with assistive software meant for the physically disabled. The algorithm incorporates a modified fuzzy competitive learning structure with a Bayesian decision rule. In order to ignore unintended behavior of the user, a Bayesian minimum risk classification rule with two loss coefficients is built into the algorithm. This provides a rational basis for outlier detection in noisy data. In addition, we show that the inclusion of a unique control parameter of IBFC allows for establishment of a strong relationship between learning region and cluster congestion. This interpretation leads to an agglomerative iterative Bayesian fuzzy clustering (AIBFC) framework capable of clustering data of complex structure. The proposed AIBFC framework is applied to design a flexible interface for the icon-based assistive software for the disabled. The latter is utilized in grouping and recommendation of icons. Additionally, the proposed algorithm is shown to outperform several well-known methods for both IRIS and Wisconsin benchmark data sets. Finally, it is shown, using a questionnaire survey of real end-users, that the software designed using AIBFC framework meets users’ needs.     

一种模糊规则提取方法的研究

提出了一种基于改进的模糊 C 均值聚类的模糊规则提取方法。然后基于所提取的模糊规则给出了一种分类算法,并利用 IRIS 数据对此分类算法进行了仿真测试。结果表明,该算法在训练祥本较少的情况下,仍能得到很好的分类效果,由此说明所提出的模糊规则生成方法有效。     

Mining fuzzy association rules for classification problems

The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The consequence part of each rule is one class label. The proposed learning algorithm consists of two phases: one to generate large fuzzy grids from training samples by fuzzy partitioning in each attribute, and the other to generate fuzzy association rules for classification problems by large fuzzy grids. The proposed learning algorithm is implemented by scanning training samples stored in a database only once and applying a sequence of Boolean operations to generate fuzzy grids and fuzzy rules; therefore, it can be easily extended to discover other types of fuzzy association rules. The simulation results from the iris data demonstrate that the proposed learning algorithm can effectively derive fuzzy association rules for classification problems.     

Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms

A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the epsiv-completeness of fuzzy rules is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules. This has two steps: First, the linear parameter matrix is adjusted, and second, the centers and widths of all membership functions are modified. The GA is introduced to identify the least important neurons, i.e., the least important fuzzy rules. Simulations are presented to illustrate the performance of the proposed algorithm     

Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control

This paper presents a systematic approach for developing a concise self-adaptive neurofuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for on-line learning of the control knowledge for autonomous underwater vehicle (AUV) control. The multi-layered network structure of SANFIS incorporates fuzzy basis functions for better function approximations. We investigate three SANFIS structures with three different types of fuzzy IF-THEN rule-based models and cast the rule formation problem as a clustering problem. A recursive least-squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the parameter learning process. This hybrid parameter learning algorithm together with an on-line clustering technique and rule examination provide SANFIS with the capability of selforganizing and self-adapting its internal structure (i.e. the fuzzy rules and term sets) for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.     

A particle swarm optimization based simultaneous learning framework for clustering and classification

A particle swarm optimization based simultaneous learning framework for clustering and classification (PSOSLCC) is proposed in this paper. Firstly, an improved particle swarm optimization (PSO) is used to partition the training samples, the number of clusters must be given in advance, an automatic clustering algorithm rather than the trial and error is adopted to find the proper number of clusters, and a set of clustering centers is obtained to form classification mechanism. Secondly, in order to exploit more useful local information and get a better optimizing result, a global factor is introduced to the update strategy update strategy of particle in PSO. PSOSLCC has been extensively compared with fuzzy relational classifier (FRC), vector quantization and learning vector quantization (VQ+LVQ3), and radial basis function neural network (RBFNN), a simultaneous learning framework for clustering and classification (SCC) over several real-life datasets, the experimental results indicate that the proposed algorithm not only greatly reduces the time complexity, but also obtains better classification accuracy for most datasets used in this paper. Moreover, PSOSLCC is applied to a real world application, namely texture image segmentation with a good performance obtained, which shows that the proposed algorithm has a potential of classifying the problems with large scale.     

Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction

This paper presents the development of fuzzy wavelet neural network system for time series prediction that combines the advantages of fuzzy systems and wavelet neural network. The structure of fuzzy wavelet neural network (FWNN) is proposed, and its learning algorithm is derived. The proposed network is constructed on the base of a set of TSK fuzzy rules that includes a wavelet function in the consequent part of each rule. A fuzzy c-means clustering algorithm is implemented to generate the rules, that is the structure of FWNN prediction model, automatically, and the gradient-learning algorithm is used for parameter identification. The use of fuzzy c-means clustering algorithm with the gradient algorithm allows to improve convergence of learning algorithm. FWNN is used for modeling and prediction of complex time series and prediction of foreign-exchange rates. Exchange rates are dynamic process that changes every day and have high-order nonlinearity. The statistical data for the last 2 years are used for the development of FWNN prediction model. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based systems and with the comparative simulation results of previous related models.