Rough sets in hybrid methods for pattern recognition

Many papers describe hybrid methods used for pattern recognition. Such systems consist of an optical part, performing fast signal preprocessing, and a computer, analyzing preprocessed data. Here we present the method which uses for feature extraction the ring‐wedge detectors (RWD) or computer generated holograms (CGH) serving as RWD. Features obtained in this way are shift, rotation, and scale invariant, but papers suggest that they can be still subject for further optimization. This article presents an original method for optimizing feature extraction abilities of CGH. This method uses rough set theory (RST) to measure the amount of essential information for the classification, preserved in feature vector. As there is no gradient direction information in factors defined by RST, we use for a space search a stochastic evolutionary approach. Finally, we use RST to determine decision rules for the feature vector classification. The whole method is illustrated by a system recognizing the speckle pattern images obtained as a result of interference of light going through a quasi‐monomode optical fiber. As the conditions of interference differ when some kind of distortion of the optical fiber is produced, such a system can be used as a sensor of the pressure causing this distortion. © 2000 John Wiley & Sons, Inc.     

A New Method of Selection and Reduction of System Feature in Pattern Recognition Based on Rough Sets

A new method of selection and reduction of system feature in pattern recognition based on rough sets is proposed in this paper. Its basic idea is that the classification ability of system feature is evaluated with the classification affection introduced by removing some redundant feature or unimportant feature in the system and comparing the final classification result to find out useful feature in pattern recognition. Eventually, reduction and optimum combination of feature sets are performed by this method. An example of selection and reduction of system feature in pattern recognition based on rough sets shows the correctness and effectiveness of the method.     

An introduction of the condition class space with continuous value discretization and rough set theory

The granularity of an information system has an incumbent effect on the efficacy of the analysis from many machine learning algorithms. An information system contains a universe of objects characterized and categorized by condition and decision attributes. To manage the concomitant granularity, a level of continuous value discretization (CVD) is often undertaken. In the case of the rough set theory (RST) methodology for object classification, the granularity contributes to the grouping of objects into condition classes with the same condition attribute values. This article exposits the effect of a level of CVD on the subsequent condition classes constructed, with the introduction of the condition class space—the domain within which the condition classes exist. This domain elucidates the association of the condition classes to the related decision outcomes—reflecting the inexactness incumbent when a level of CVD is undertaken. A series of measures is defined that quantify this association. Throughout this study and without loss of generality, the findings are made through the RST methodology. This further offers a novel exposition of the relationship between all the condition attributes and the RST‐related reducts (subsets of condition attributes). © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 173–191, 2006.     

基于粗糙集理论的模式分类样本特征选择方法研究

本文提出了一种基于粗糙集理论的模式分类本特征选择方法，该方法应用粗糙集理论和方法，对给定的学习样本进行特征选择，根据这些特征构造神经网络模型进行训练，并在网络的工作阶段，根据这些特征对待识样本进行分类，在模式分类中，该方法能够减少网络的训练时间并改善网络的泛化能力。     

粗糙集的特征选择在客户关系管理中的应用

介绍了粗糙集理论的特征选择在客户关系管理中的应用.分析了以前特征选择方法中由于特征数目过多而造成分类时间和精度不高的缺点,提出了一种基于粗糙集的特征选择方法,其特点是以特征在分类中的重要性对特征进行选择.对客户关系管理进行了研究,为解决客户关系管理问题提出了一个新的方法,基于粗糙集的特征选择方法与传统的特征选择方法相比也能达到降维的目的.最后实验结果表明该算法是可行的.     

A NEW E‐LEARNING ACHIEVEMENT EVALUATION MODEL BASED ON ROUGH SET AND SIMILARITY FILTER

The trend of utilizing information and Internet technologies as teaching and learning tools is rapidly expanding into education. E‐learning is one of the most popular learning environments in the information era. The Internet enables students to learn without limitations of space and time. Furthermore, the learners can repeatedly review the context of a course without the barrier of distance. Recently, student‐centered instruction has become the primary trend in education, and the e‐learning system, which is considered with regard to of personalization and adaptability, is more and more popular. By means of e‐learning systems, teachers can adjust the learning schedule instantly for each learner according to a student's achievements and build more adaptive learning environments. Sometimes, teachers give biased assessments of students’ achievements under uncontrollable conditions (i.e., tiredness, preference) and are in dire need of overcoming this predicament. To solve the drawback mentioned, a new model to evaluate learning achievements based on rough set and similarity filter is proposed. The proposed model includes four facets: (1) select important features (attributes) to enhance classification performance by feature selection methods; (2) utilize minimal entropy principle approach (MEPA) to fuzzify the quantitative data; (3) select linguistic values for each feature and delete inconsistent data using the similarity threshold (similarity filter); and (4) generate rules based on rough set theory (RST). The practical e‐learning achievement data sets are collected by an e‐learning online examination system from a university in Taiwan. To verify our model, the performances of the proposed model are compared with the listing models. Results of this study demonstrate that the proposed model outperforms the listing models.     

基于中心距离比值准则的无监督特征选择算法

特征选择是模式识别中的一个重要组成部分。针对未知类标号的样本集,提出基于中心距离比值准则的无监督特征选择算法。该算法利用爬山法确定聚类数目范围和估计初始聚类中心,再通过K-均值聚类算法确定特征子集的最佳分类数,然后用中心距离比值准则来评价特征子集的分类性能,并通过特征间的相关性分析,从中选择出分类效果好,相关程度低的特征组成特征子集。     

Signal‐to‐noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

Abstract: Features are used to represent patterns with minimal loss of important information. The feature vector, which is composed of the set of all features used to describe a pattern, is a reduced‐dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal‐to‐noise ratio saliency measure was employed to determine the saliency of input features of recurrent neural networks (RNNs) used in classification of ophthalmic arterial Doppler signals. Eigenvector methods were used to extract features representing the ophthalmic arterial Doppler signals. The RNNs used in the ophthalmic arterial Doppler signal classification were trained for the signal‐to‐noise ratio screening method. The application results of the signal‐to‐noise ratio screening method to the ophthalmic arterial Doppler signals demonstrated that classification accuracies of RNNs with salient input features are higher than those of RNNs with salient and non‐salient input features.     

On Taxonomy and Evaluation of Feature Selection‐Based Learning Classifier System Ensemble Approaches for Data Mining Problems

Ensemble methods aim at combining multiple learning machines to improve the efficacy in a learning task in terms of prediction accuracy, scalability, and other measures. These methods have been applied to evolutionary machine learning techniques including learning classifier systems (LCSs). In this article, we first propose a conceptual framework that allows us to appropriately categorize ensemble‐based methods for fair comparison and highlights the gaps in the corresponding literature. The framework is generic and consists of three sequential stages: a pre‐gate stage concerned with data preparation; the member stage to account for the types of learning machines used to build the ensemble; and a post‐gate stage concerned with the methods to combine ensemble output. A taxonomy of LCSs‐based ensembles is then presented using this framework. The article then focuses on comparing LCS ensembles that use feature selection in the pre‐gate stage. An evaluation methodology is proposed to systematically analyze the performance of these methods. Specifically, random feature sampling and rough set feature selection‐based LCS ensemble methods are compared. Experimental results show that the rough set‐based approach performs significantly better than the random subspace method in terms of classification accuracy in problems with high numbers of irrelevant features. The performance of the two approaches are comparable in problems with high numbers of redundant features.     

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

The degree of malignancy in brain glioma is assessed based on magnetic resonance imaging (MRI) findings and clinical data before operation. These data contain irrelevant features, while uncertainties and missing values also exist. Rough set theory can deal with vagueness and uncertainty in data analysis, and can efficiently remove redundant information. In this paper, a rough set method is applied to predict the degree of malignancy. As feature selection can improve the classification accuracy effectively, rough set feature selection algorithms are employed to select features. The selected feature subsets are used to generate decision rules for the classification task. A rough set attribute reduction algorithm that employs a search method based on particle swarm optimization (PSO) is proposed in this paper and compared with other rough set reduction algorithms. Experimental results show that reducts found by the proposed algorithm are more efficient and can generate decision rules with better classification performance. The rough set rule-based method can achieve higher classification accuracy than other intelligent analysis methods such as neural networks, decision trees and a fuzzy rule extraction algorithm based on Fuzzy Min-Max Neural Networks (FRE-FMMNN). Moreover, the decision rules induced by rough set rule induction algorithm can reveal regular and interpretable patterns of the relations between glioma MRI features and the degree of malignancy, which are helpful for medical experts.     

Phase‐correlation‐based hyperspectral image classification using multiple class representatives obtained with k‐means clustering

In this letter, a modification to a phase–correlation‐(PC‐)based supervised classification method for hyperspectral data is proposed. An adaptive approach using different numbers of multiple class representatives (CRs) extracted using PC‐based k‐means clustering for each class is compared with the use of selecting a small, pre‐determined number of dissimilar CRs. PC is used as a distance measure in k‐means clustering to determine the spectral similarity between each pixel and cluster centre. The number of representatives for each class is chosen adaptively, depending on the number of training samples in each class. Classification is performed for each pixel according to the maximum value of PCs obtained between test samples and the CRs. Experimental results show that the adaptive method gave the highest classification accuracy (CA). Experiments on the effect of reducing the size of the feature vectors found that CA increased as the feature vector decreased.     

Exploring the boundary region of tolerance rough sets for feature selection

Of all of the challenges which face the effective application of computational intelligence technologies for pattern recognition, dataset dimensionality is undoubtedly one of the primary impediments. In order for pattern classifiers to be efficient, a dimensionality reduction stage is usually performed prior to classification. Much use has been made of rough set theory for this purpose as it is completely data-driven and no other information is required; most other methods require some additional knowledge. However, traditional rough set-based methods in the literature are restricted to the requirement that all data must be discrete. It is therefore not possible to consider real-valued or noisy data. This is usually addressed by employing a discretisation method, which can result in information loss. This paper proposes a new approach based on the tolerance rough set model, which has the ability to deal with real-valued data whilst simultaneously retaining dataset semantics. More significantly, this paper describes the underlying mechanism for this new approach to utilise the information contained within the boundary region or region of uncertainty. The use of this information can result in the discovery of more compact feature subsets and improved classification accuracy. These results are supported by an experimental evaluation which compares the proposed approach with a number of existing feature selection techniques.     

Semiconormed possibility integrals and multi-feature pattern classification

The semiconormed possibility integrals are proposed as a multi-feature pattern classification model. A semiconormed possibility integral is a nonlinear integration of a function and its corresponding non-normalized possibility measures over feature space. The function of an object's feature vector represents the possibilities with uncertainty that the object belongs to a class. The uncertainty is due to the similar characteristics of objects from different classes and the distortion of the original characteristic information caused by feature data acquisition systems. The uncertainty is assessed by the non-normalized possibility measures, a possibility measure of a feature is considered as the credibility of the feature to provide reliable information for pattern classification. Integration of a function and the possibility measures effectively reduces the uncertainty and improves the pattern classification results. A pattern classification algorithm based on the semiconormed possibility integrals was used to classify a set of “ellipse data” and the well-known IRIS data, the classification results were compared with those obtained by using Bayes classifier.     

SAR‐based land cover classification of Kuwait

Orbital synthetic aperture radar (SAR) C‐band data acquired by ERS‐1/2 in vv‐polarization and Radarsat in hh‐polarization during the period from 1996 to 1999 were used to evaluate their combined information potential for classification of land cover in the arid environment of Kuwait. Individual SAR scenes were orthorectified using a digital elevation model (DEM) of Kuwait, radiometrically adjusted for incidence angle effects, and mosaics were generated for the whole country. The data were coregistered as multichannel composites and integrated with geographical information system (GIS) layers of roads, hydrology, soils and vegetation. An adaptive spatial filter was used to increase the number of effective independent looks prior to generation of feature vectors based on SAR backscatter power values. A total of 13 classes of the joint ERS‐1/2 and Radarsat images were identified based on Bhattacharya distance and geospatial pattern. The C‐band radar backscatter observed by ERS and Radarsat was found to be related to vegetation cover, surface roughness, percentage of coarse material in the surface layer and moisture conditions. These factors are not independent, but are known to be correlated. The complexity of these dependencies made unambiguous classification of surface material difficult when using C‐band data alone. Nevertheless, class labels were assigned using a maximum likelihood supervised classification incorporating field measurements and ancillary data such as soil, and surface sediment maps. When used in a simple two‐class classification (e.g. low vs. high vegetation cover fraction, or smooth vs. rough soils), the overall accuracy of the combined ERS and Radarsat data was between 70 and 80%. The generated dataset is amenable to several label definitions based on the requirements of the intended use.     

无监督模式下统计不相关最佳鉴别平面

统计不相关最佳鉴别平面是一种重要的特征抽取方法,在模式识别领域中具有广泛的应用。然而,统计不相关最佳鉴别平面是基于Fisher准则和总体散布矩阵共轭正交条件的,需要通过样本类别信息计算Fisher最佳鉴别矢量,因而只能用于有监督模式。提出了一种将统计不相关最佳鉴别平面扩展到无监督模式下的方法,其基本思想是将模糊概念引入Fisher线性判别分析,通过对模糊Fisher准则的优化,在无监督模式下计算出最佳鉴别矢量及模糊散布矩阵,再根据共轭正交约束条件,求得第二条最佳鉴别矢量,进而获得一种基于无监督统计不相关最佳鉴别平面的特征抽取方法。对UCI数据集及CMU-PIE人脸数据库进行实验,结果表明,在样本类别信息缺失的情况下,该方法尽管无法具有与有监督模式下的统计不相关最佳鉴别平面特征抽取方法同样的性能,但当类别差异较大时,能够抽取有利于分类的统计不相关特征,获得优于主成分分析与独立成分分析等常见无监督特征抽取方法的性能。     

基于知识的模型自动选择策略

模型自动选择是决策支持系统智能化发展的必然要求。针对目前实用算法较少的现状,提出一种模型自动选择策略。基于知识框架描述模型,根据事实库和知识库提取相应规则生成推理树,结合经验和专业知识实现模型自动选择。实验结果表明,该策略具有较高的命中率。     

基于粗糙集的决策树构造算法

针对ID3算法构造决策树复杂、分类效率不高问题,基于粗糙集理论提出一种决策树构造算法。该算法采用加权分类粗糙度作为节点选择属性的启发函数,与信息增益相比,能全面地刻画属性分类的综合贡献能力,并且计算简单。为消除噪声对选择属性和生成叶节点的影响,利用变精度粗糙集模型对该算法进行优化。实验结果表明,该算法构造的决策树在规模与分类效率上均优于ID3算法。     

Electroencephalography‐based feature extraction using complex network for automated epileptic seizure detection

Electroencephalography signals are typically used for analyzing epileptic seizures. These signals are highly nonlinear and nonstationary, and some specific patterns exist for certain disease types that are hard to develop an automatic epileptic seizure detection system. This paper discussed statistical mechanics of complex networks, which inherit the characteristic properties of electroencephalography signals, for feature extraction via a horizontal visibility algorithm in order to reduce processing time and complexity. The algorithm transforms a time series signal into a complex network, which some features are abbreviated. The statistical mechanics are calculated to capture distinctions pertaining to certain diseases to form a feature vector. The feature vector is classified by multiclass classification via a k‐nearest neighbor classifier, a multilayer perceptron neural network, and a support vector machine with a 10‐fold cross‐validation criterion. In performance evaluation of proposed method with healthy, seizure‐free interval, and seizure signals, firstly, input data length is regarded among some practical signal samples by optimizing between accuracy‐processing time, and the proposed method yields outstanding performance on the average classification accuracy for 3‐class problems mainly for detection of seizure‐free interval and seizure signals and acceptable results for 2‐class and 5‐class problems comparing with conventional methods. The proposed method is another tool that can be used for classifying signal patterns, as an alternative to time/frequency analyses.     

基于特征相关性的特征选择

提出了一种基于特征相关性的特征选择方法。该方法以特征之间相互依赖程度（相关度）为聚类依据先对特征进行聚类,再从各特征簇中挑选出具有代表性的特征,然后在被选择出来的特征中删除与目标特征无关或是弱相关的特征,最后留下的特征作为最终的特征子集。理论分析表明该方法的运算效率高,时间复杂度低,适合于大规模数据集中的特征选择。在UCI数据集上与文献中的经典方法进行实验比较和分析,结果显示提出的特征选择方法在特征约减和分类等方面具有更好的性能。     

用组合不变量进行模式识别的一种新方法

用与目标的位置、大小、方向和其他变化无关的特征来识别目标是模式识别领域的一个热点。现存的基于不变特征的二维模式识别方法在目标被模糊了情况下都无法精确识别。本文提出了一种可解决上述问题的新的模式识别方法。该方法用组合不变量作为图像特征,以加权规格化互相关作为分类技术。在分类过程中,使用每一类的所有原型的第k个特征的类内标准方差的均值作为加权因子以提高识别率。对头像的数字试验证实了组合不变量特征对图像的平移、伸缩、旋转和模糊变换的不变性和该模式识别方法的可行性。