Modified linear discriminant analysis approaches for classification of high-dimensional microarray data

Linear discriminant analysis (LDA) is one of the most popular methods of classification. For high-dimensional microarray data classification, due to the small number of samples and large number of features, classical LDA has sub-optimal performance corresponding to the singularity and instability of the within-group covariance matrix. Two modified LDA approaches (MLDA and NLDA) were applied for microarray classification and their performance criteria were compared with other popular classification algorithms across a range of feature set sizes (number of genes) using both simulated and real datasets. The results showed that the overall performance of the two modified LDA approaches was as competitive as support vector machines and other regularized LDA approaches and better than diagonal linear discriminant analysis, k-nearest neighbor, and classical LDA. It was concluded that the modified LDA approaches can be used as an effective classification tool in limited sample size and high-dimensional microarray classification problems.     

三种训练光滑支持向量分类器方法的比较

光滑支持向量分类机（SSVC）是支持向量分类机（SVC）的快速求解模型,本质上是求解数学规划中具有光滑性和强凸性的无约束最优化问题。BFGS-Armijo和Newton-Armijo算法被用来训练SSVC,相比而言后者拥有更快的训练速度;牛顿-预优共轭梯度法（Newton-PCG）适用于求解无约束的最优化问题,理论上快于一般的Newton类算法。使用Newton-Armijo、BFGS-Armijo和Newton-PCG三种算法来训练光滑支持向量分类机,根据数值实验结果进行分析比较,证明了Newton-PCG算法有更优的效果。     

Comparison of two different approaches for making design sensitivity analysis an integrated part of finite element analysis

This paper presents a comparison of two approaches for incorporating design sensitivity calculations into finite element analysis. The formulations depend on the implicit differentiation method and require few additional calculations to obtain the design sensitivity derivatives. The first approach by-passes the direct calculation of the stiffness matrix derivatives by calculating the sensitivity derivatives at the stress recovery stage of the analysis. The second approach depends on the direct calculation of the derivatives of the stiffness matrix, which are stored for re-use in multiple load case analyses, and subsequent matrix multiplications to evaluate the design sensitivities. The two approaches are developed and implemented to calculate the design sensitivities for continuum and structural isoparametric elements. In addition, a criterion is developed to aid in deciding which approach is better for a given number of load cases. To demonstrate the validity of the developed criterion and to evaluate the relative merits of each approach, some sensitivity calculation test problems are solved with different numbers of load cases.     

Comparison of different classification algorithms for underwater target discrimination

Classification of underwater targets from the acoustic backscattered signals is considered. Several different classification algorithms are tested and benchmarked not only for their performance but also to gain insight to the properties of the feature space. Results on a wideband 80-kHz acoustic backscattered data set collected for six different objects are presented in terms of the receiver operating characteristic (ROC) and robustness of the classifiers wrt reverberation.     

融合NBC与PNN的网络异常分类

对网络异常进行分类有利于管理员更好地管理网络,然而单一的分类器存在对各类异常的分类效果不均衡,不够全面等问题。鉴于此在研究了常用于分类的概率神经网络（Probability Neural Network,PNN）算法和朴素贝叶斯分类器（Naive Bayes Classifier,NBC）算法的基础上提出了一种融合NBC与PNN的网络异常分类模型。该模型将PNN与NBC对各类网络异常的分类精度作为权值,通过计算得出未知流量所属各类别的概率,最大值为预测结果,通过KDD99数据集对该模型进行测试,实验结果表明,提出的新模型相对于仅使用PNN或者NBC的单分类器,其对各类异常的分类效果具有更好的均衡性和更高的分类精度。     

基于密度的kNN分类器训练样本裁剪方法的改进

在文本分类中,训练集的分布状态会直接影响k-近邻(kNN)分类器的效率和准确率。通过分析基于密度的kNN文本分类器训练样本的裁剪方法,发现它存在两大不足：一是裁剪之后的均匀状态只是以ε为半径的球形区域意义上的均匀状态,而非最理想的均匀状态即两两样本之间的距离相等;二是未对低密度区域的样本做任何处理,裁剪之后仍存在大量不均匀的区域。针对这两处不足,提出了以下两点改进：一是优化了裁剪策略,使裁剪之后的训练集更趋于理想的均匀状态;二是实现了对低密度区域样本的补充。通过实验对比,改进后的方法在稳定性和准确率方面都有明显提高。     

Discriminative training approaches to fabric defect classification based on wavelet transform

Wavelet transform is able to characterize the fabric texture at multiscale and multiorientation, which provides a promising way to the classification of fabric defects. For the objective of minimum error rate in the defect classification, this paper compares six wavelet transform-based classification methods, using different discriminative training approaches to the design of the feature extractor and classifier. These six classification methods are: methods of using an Euclidean distance classifier and a neural network classifier trained by maximum likelihood method and backpropagation algorithm, respectively; methods of using an Euclidean distance classifier and a neural network classifier trained by minimum classification error method, respectively; method of using a linear transformation matrix-based feature extractor and an Euclidean distance classifier, designed by discriminative feature extraction (DFE) method; method of using an adaptive wavelet-based feature extractor and an Euclidean distance classifier, designed by the DFE method. These six approaches have been evaluated on the classification of 466 defect samples containing eight classes of fabric defects, and 434 nondefect samples. The DFE training approach using adaptive wavelet has been shown to outperform the other approaches, where 95.8% classification accuracy was achieved.     

A comparison of different Gabor feature extraction approaches for mass classification in mammography

We investigate the performance of six different approaches for directional feature extraction for mass classification problem in digital mammograms. These techniques use a bank of Gabor filters to extract the directional textural features. Directional textural features represent structural properties of masses and normal tissues in mammograms at different orientations and frequencies. Masses and micro-calcifications are two early signs of breast cancer which is a major leading cause of death in women. For the detection of masses, segmentation of mammograms results in regions of interest (ROIs) which not only include masses but suspicious normal tissues as well (which lead to false positives during the discrimination process). The problem is to reduce the false positives by classifying ROIs as masses and normal tissues. In addition, the detected masses are required to be further classified as malignant and benign. The feature extraction approaches are evaluated over the ROIs extracted from MIAS database. Successive Enhancement Learning based weighted Support Vector Machine (SELwSVM) is used to efficiently classify the generated unbalanced datasets. The average accuracy ranges from 68 to 100 % as obtained by different methods used in our paper. Comparisons are carried out based on statistical analysis to make further recommendations.     

Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution

For remote-sensing applications such as spectra classification or identification, atmospheric correction constitutes a very important pre-processing step, especially in complex urban environments where a lot of phenomenons alter the shape of the signal. The objective of this article is to compare the efficiency of two atmospheric correction algorithms, COCHISE (atmospheric COrrection Code for Hyperspectral Images of remote-sensing SEnsors) and an empirical method, on hyperspectral data and for classification applications. Classification is carried out on several simulated spaceborne data sets with different spatial resolutions (from 1.6 to 9.6 m). Four classifiers are considered in the study: a k-means, a Support Vector Machine (SVM), and a sun/shadow version of each of them, which processes sunlit and shadowed pixels separately. Results show that the most relevant atmospheric method for classification depends on the spatial resolution of the processed data set. Indeed, if the empirical method performs better on high-resolution data sets (up to 4%), its superiority fades out as the spatial resolution decreases, especially with the lower spatial resolution where COCHISE can be 10% more accurate than the empirical method.     

信息检索中的两个数据融合方法比较

讨论一种常见的集成方法--距离平方和最小准则,指出该准则下由线性加权原理所得融合结果的优良性以及信息检索文献中的一个错误.然后通过分析基于距离之和最小准则所得融合结果的检索性能,发现由基于距离之和最小准则得到的融合结果距离原始检索结果最近.最后,通过实例验证了该方法的结果.     

Numerical simulations for the evaluation of the free surface history in porous media. Comparison between two different approaches

This paper shown an empirical comparison of two different models for the theoretical analysis of the zero pressure surface position during the imbition of a partially saturated porous medium: the model based on the unsaturated-saturated approach, and the model which describes the zero pressure surface as a free external boundary for the fully saturated zone. A domain FD technique is applied for solving the governing equations of the first model, whereas a BEM formulation has been chosen for the equations of the second one. The comparison is based on numerical experiments which deal with the imbition of an academic system. The results obtained highlight the tendency of the fully saturated approximation to provide solutions where the rise of the free surface is much more localized near the injection zone than in the case of the unsaturated-saturated approach. In addition, owing to the capability of describing the capillary fringe, these latter solutions affect thicker layers of porous matrix. So, the users have to consider the system of interest with the greatest care before they choose the mathematical model which is the most suitable for the aims of the planned simulation.     

Comparison of different classification algorithms in clinical decision-making

Abstract: This paper gives an integrated view of implementing automated diagnostic systems for clinical decision-making. Because of the importance of making the right decision, better classification procedures are necessary for clinical decisions. The major objective of the paper is to be a guide for readers who want to develop an automated decision support system for clinical practice. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and benchmarked for their performance. The performance of the classification algorithms is illustrated on two data sets: the Pima Indians diabetes and the Wisconsin breast cancer. The present research demonstrates that the support vector machines achieved diagnostic accuracies which were higher than those of other automated diagnostic systems.     

Integration of IRS-1A L2 data by fuzzy logic approaches for landuse classification

A methodology has been formulated to integrate images from IRS-1A LISS II of two dates for landuse/landcover classification. The methodology developed includes image classification by fuzzy k-means clustering and fusion of memberships by fuzzy set theoretic operators. The two date images have been geometrically coregistered and classified for the identification of land classes individually. The fuzzy memberships of the classified output images have been integrated by using fuzzy logic operators like algebraic sum and gamma (gamma) operator. The classification accuracy of the resultant land classes in the integrated images was verified with the ground data collected in situ. The resultant images have been evaluated by kappa (kappa) statistic and it was found that output from the image of fuzzy algebraic sum operator scored high in generating the land classes, with an overall accuracy of 95%.     

Comparison of two scaling approaches for the development of biomechanical multi-body human models

Dimensional scaling approaches are widely used to develop multi-body human models in injury biomechanics research. Given the limited experimental data for any particular anthropometry, a validated model can be scaled to different sizes to reflect the biological variance of population and used to characterize the human response. This paper compares two scaling approaches at the whole-body level: one is the conventional mass-based scaling approach which assumes geometric similarity; the other is the structure-based approach which assumes additional structural similarity by using idealized mechanical models to account for the specific anatomy and expected loading conditions. Given the use of exterior body dimensions and a uniform Young’s modulus, the two approaches showed close values of the scaling factors for most body regions, with 1.5 % difference on force scaling factors and 13.5 % difference on moment scaling factors, on average. One exception was on the thoracic modeling, with 19.3 % difference on the scaling factor of the deflection. Two 6-year-old child models were generated from a baseline adult model as application example and were evaluated using recent biomechanical data from cadaveric pediatric experiments. The scaled models predicted similar impact responses of the thorax and lower extremity, which were within the experimental corridors; and suggested further consideration of age-specific structural change of the pelvis. Towards improved scaling methods to develop biofidelic human models, this comparative analysis suggests further investigation on interior anatomical geometry and detailed biological material properties associated with the demographic range of the population.     

Increasing the accuracy of neural network classification using refined training data

Image classification is a complex process affected by some uncertainties and decisions made by the researchers. The accuracy achieved by a supervised classification is largely dependent upon the training data provided by the analyst. The use of representative training data sets is of significant importance for the performance of all classification methods. However, this issue is more important for neural network classifiers since they take each sample into consideration in the training stage. The representativeness is related to the size and quality of the training data that are highly important in assessing the accuracy of the thematic maps derived from remotely sensed data. Quality analysis of training data helps to identify outlier and mixed pixels that can undermine the reliability and accuracy of a classification resulting from an incorrect class boundary definition. Training data selection can be thought of as an iterative process conducted to form a representative data set after some refinements. Unfortunately, in many applications the quality of the training data is not questioned, and the data set is directly employed in the training stage. In order to increase the representativeness of the training data, a two-stage approach is presented, and performance tests are conducted for a selected region. Multi-layer perceptron model trained with backpropagation learning algorithm is employed to classify major land cover/land use classes present in the study area, the city of Trabzon in Turkey. Results show that the use of representative training data can help the classifier to produce more accurate and reliable results. An improvement of several percent in classification accuracy can make significant effect on the quality of the classified image. Results also confirm the value of visualization tools for the assessment of training pixels through decision boundary analysis.     

Comparative analysis of two different middleware approaches for reconfiguration of distributed real-time systems

Software-based reconfiguration of distributed real-time systems is a complex problem with many sides to it ranging from system-wide concerns down to the intrinsic non-robust nature of the specific middleware layer and the used programming techniques. In a completely open distributed system, mixing reconfiguration and real-time is not possible; the set of possible target states can be very large threatening the temporal predictability of the reconfiguration process. Over the last years, middle ware solutions have appeared mainly for general purpose systems where efficient state transitions are sought for, but real-time properties are not considered. One of the few contributions to run-time software reconfiguration in distributed real-time environments has been the iLAND middleware, where the germ of a solution with high potential has been conceived and delivered in practice.¹ The key idea has been the fact that a set of bounds and limitations to the structure of systems and to their open nature needs to be imposed in order to come up with practical solutions. In this paper, the authors present the different sides of the problem of software reconfiguration from two complementary middleware perspectives comparing two strategies built inside distribution middleware. We highlight the lessons learned in the iLAND project aimed at service-based reconfiguration and compare it to our experience in the development of distributed real-time Java reconfiguration based on distributed tasks rescheduling. Authors also provide a language view of both solutions. Lastly, empirical results are shown that validate these solutions and compare them on the basis of different programming language realizations.     

基于PHP实现数据安全性的方法及比较

讨论了多种在LAMP(Linux+Apache+Mysqi+PHP)平台下,使用PHP(超文本预处理器)实现对待下载的文件进行加密并在下载后解密的方法,介绍了PHP以及LAMP平台的应用情况;研究和介绍了在LAMP平台下,通过调用PHP内置函数、扩展/类库以及GnuPG(GNU privacy guard)软件实现数据加密解密的方法,并比较分析了不同方法之间的优缺点,给出了相应的示例代码,以及针对各种方法的比较分析和实验结果;讨论了如何选择不同的加密方法来适用于不同的具体环境,并指出了PHP加密的局限性.     

Building simulation approaches for the training of automated data analysis tools in building energy management

The field of building energy management, which monitors and analyses the energy use of buildings with the aim to control and reduce energy expenditure, is seeing a rapid evolution. Automated meter reading approaches, harvesting data at hourly or even half-hourly intervals, create a large pool of data which needs analysis. Computer analysis by means of machine learning techniques allows automated processing of this data, invoking expert analysis where anomalies are detected. However, machine learning always requires a historical dataset to train models and develop a benchmark to define what constitutes an anomaly. Computer analysis by means of building performance simulation employs physical principles to predict energy behaviour, and allows the assessment of the behaviour of buildings from a pure modelling background. This paper explores how building simulation approaches can be fused into energy management practice, especially with a view to the production of artificial bespoke benchmarks where historical profiles are not available. A real accommodation block, which is subject to monitoring, is used to gather an estimation of the accuracy of this approach. The findings show that machine learning from simulation models has a high internal accuracy; comparison with actual metering data shows prediction errors in the system (20%) but still achieves a substantial improvement over industry benchmark values.     

A comparison of two approaches to utilizing XML in parametric databases for temporal data

The parametric data model captures an object in terms of a single tuple. This feature eliminates unnecessary self-join operations to combine tuples scattered in a temporal relation. Despite this advantage, this model is relatively difficult to implement on top of relational databases because the sizes of attributes are unfixed. Since data boundaries are not problematic in XML, XML can be an elegant solution to implement parametric databases for temporal data. There are two approaches to implementing parametric databases using XML: (1) a native XML database with XQuery engine, and (2) an XML storage with a temporal query language. To determine which approach is appropriate in parametric databases, we consider four questions: the effectiveness of XML in modeling temporal data, the applicability of XML query languages, the user-friendliness of the query languages, and system performances of two approaches. By evaluating the four questions, we show that the latter approach is more appropriate to utilizing XML in parametric databases.