Ensemble classification based on supervised clustering for credit scoring

Credit scoring aims to assess the risk associated with lending to individual consumers. Recently, ensemble classification methodology has become popular in this field. However, most researches utilize random sampling to generate training subsets for constructing the base classifiers. Therefore, their diversity is not guaranteed, which may lead to a degradation of overall classification performance. In this paper, we propose an ensemble classification approach based on supervised clustering for credit scoring. In the proposed approach, supervised clustering is employed to partition the data samples of each class into a number of clusters. Clusters from different classes are then pairwise combined to form a number of training subsets. In each training subset, a specific base classifier is constructed. For a sample whose class label needs to be predicted, the outputs of these base classifiers are combined by weighted voting. The weight associated with a base classifier is determined by its classification performance in the neighborhood of the sample. In the experimental study, two benchmark credit data sets are adopted for performance evaluation, and an industrial case study is conducted. The results show that compared to other ensemble classification methods, the proposed approach is able to generate base classifiers with higher diversity and local accuracy, and improve the accuracy of credit scoring.     

基于监督局部线性嵌入特征提取的高光谱图像分类

高光谱图像的数据维数高、数据量大、数据间高度冗余等特点给图像分类带来困难,为进行有效降维、提高分类精度,提出了一种监督局部线性嵌入(SLLE)非线性流形学习特征提取方法。SLLE算法根据数据先验类标签信息所给出的新距离寻找数据点的k最近邻(NN),新距离使得类内距离小于类间距离,这使得SLLE算法更有利于分类。高光谱图像数据和UCI数据的分类结果表明了该方法的有效性。     

基于声传感器线性阵列的气流速度估计算法

研究了一种基于声传感器线性阵列的新型气流速度测量方法.通过引入大气声学中的有效声速概念,建立了稳定气流作用下各阵元的接收模型,由此建立了声传感器线性阵列的近场输出模型.根据子空间正交原理,提出了一种基于多重信号分类(MUSIC)的气流速度估计(MUSIC-AVE)算法,此算法可实现对气流速度的高精度估计.为了降低计算复杂度,进一步提出了一种快速的气流速度估计(FAVE)算法,此算法虽然在估计精度上不如MUSIC-AVE算法,但无需谱搜索,具有更强的实时性.推导了气流速度估计的克拉美-罗界(CRB)表达式.仿真实验验证了提出算法的有效性.     

Using support vector machines and acoustic noise signal for degradation analysis of rotating machinery

An automated approach to degradation analysis is proposed that uses a rotating machine’s acoustic signal to determine Remaining Useful Life (RUL). High resolution spectral features are extracted from the acoustic data collected over the entire lifetime of the machine. A novel approach to the computation of Mutual Information based Feature Subset Selection is applied, to remove redundant and irrelevant features, that does not require class label boundaries of the dataset or spectral locations of developing defect to be known or pre-estimated. Using subsets of the feature space, multi-class linear and Radial Basis Function (RBF) Support Vector Machine (SVM) classifiers are developed and a comparison of their performance is provided. Performance of all classifiers is found to be very high, 85 to 98%, with RBF SVMs outperforming linear SVMs when a smaller number of features are used. As larger numbers of features are used for classification, the problem space becomes more linearly separable and the linear SVMs are shown to have comparable performance. A detailed analysis of the misclassifications is provided and an approach to better understand and interpret costly misclassifications is discussed. While defining class label boundaries using an automated k-means clustering algorithm improves performance with an accuracy of approximately 99%, further analysis shows that in 88% of all misclassifications the actual class of failure had the next highest probability of occurring. Thus, a system that incorporates probability distributions as a measure of confidence for the predicted RUL would provide additional valuable information for scheduling preventative maintenance. This work was supported by IDA Ireland.     

A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification

ABSTRACT

Recently, precise and deterministic feature extraction is one of the current research topics for bearing fault diagnosis. For this aim, an experimental bearing test setup was created in this study. In this setup, vibration signals were obtained from the bearings on which artificial faults were generated in specific sizes. A new feature extraction method based on co-occurrence matrices for bearing vibration signals was proposed instead of the conventional feature extraction methods, as in the literature. The One (1) Dimensional–Local Binary Patterns (1D-LBP) method was first applied to bearing vibration signals, and a new signal whose values ranged between 0–255 was obtained. Then, co-occurrence matrices were obtained from these signals. The correlation, energy, homogeneity, and contrast features were extracted from these matrices. Different machine learning methods were employed with these features to carry out the classification process. Three different data sets were used to test the proposed approach. As a result of analysing the signals with the proposed model, the success rate is 87.50% for dataset1 (different speed), 96.5% for dataset2 (fault size (mm)) and 99.30% for dataset3 (fault type – inner ring, outer ring, ball) was found, respectively.     

我国雷达信号处理研究文献的计量统计分析

采用文献计量学的原理,以CNKI数据库中的中国期刊全文数据库作为统计源,对1999年～2008年收录在我国核心期刊上的雷达信号处理研究领域的论文,从时间分布、期刊分布、关键词词频、论文作者统计、合著分布及作者机构分布等方面进行统计分析,从一个侧面反映我国雷达信号处理研究的现状和发展趋势。     

基于TFD联合要素的图像分类

文中提出了基于时间频率分布联合要素的非平稳时间序列信号的分类方法,其结果显示：对于非平时间序列信号,基于时间频率分布联合要素的分类法比单独基于时间或频率分布要素的方法处理效果好。     

Remote estimation of chlorophyll a in optically complex waters based on optical classification

Accurate assessment of phytoplankton chlorophyll a (Chla) concentration in turbid waters by means of remote sensing is challenging due to optically complexity and significant variability of case 2 waters, especially in inland waters with multiple optical types. In this study, a water optical classification algorithm is developed, and two semi-analytical algorithms (three- and four-band algorithm) for estimating Chla are calibrated and validated using four independent datasets collected from Taihu Lake, Chaohu Lake, and Three Gorges Reservoir. The optical classification algorithm is developed using the dataset collected in Taihu Lake from 2006 to 2009. This dataset is also used to calibrate the three- and four-band Chla estimation algorithms. The optical classification technique uses remote sensing reflectance at three bands: Rrs(G), Rrs(650), and Rrs(NIR), where G indicates the location of reflectance peak in the green region (around 560 nm), and NIR is the location of reflectance peak in the near-infrared region (around 700 nm). Optimal reference wavelengths of the three- and four-band algorithm are located through model tuning and accuracy optimization. The three- and four-band algorithm accuracy is further evaluated using other three independent datasets. The improvement of optical classification in Chla estimation is revealed by comparing the performance of the two algorithms for non-classified and classified waters.Using the slopes of the three reflectance bands, the 138 reflectance spectra samples in the calibration dataset are classified into three classes, each with a specific spectral shape character. The three- and four-band algorithm performs well for both non-classified and classified waters in estimating Chla. For non-classified waters, strong relationships are yielded between measured and predicted Chla, but the performance of the two algorithms is not satisfactory in low Chla conditions, especially for samples with Chla below 30 mg m^− 3. For classified waters, the class-specific algorithms perform better than for non-classified waters. Class-specific algorithms reduce considerable mean relative error from algorithms for non-classified waters in Chla predicting. Optical classification makes that there is no need to adjust the optimal position to estimate Chla for other waters using the class-specific algorithms. The findings in this study demonstrate that optical classification can greatly improve the accuracy of Chla estimation in optically complex waters.     

基于小波分解的磨床声发射特征信号提取方法

磨床磨削加工环境复杂,传统的信号分析方法难以对其进行有效的特征信号提取,提出了一种基于小波分解与能量谱相结合的特征信号提取方法.利用小波多尺度、多分辨率的特性,对磨床磨削的声发射信号进行多尺度分解;根据金属磨削的声发射信号的特性,选取最优频率段进行频谱分析,再结合能量谱提取特征信号.通过对自行研发的磨削监测系统工程试验分析表明:该方法能够有效、准确地诊断出磨削的每个状态,误判率为0.02％,相比单一的频谱分析诊断,精度更高、可靠性更好,具有一定的工程实用价值.     

DOA estimation based on the difference and sum coarray for coprime arrays

In this paper, we construct a novel coarray named as the difference and sum (diff–sum) coarray by exploiting an improved Conjugate Augmented MUSIC (CAM) estimator, which utilizes both the temporal information and the spatial information. The diff–sum coarray is the union of the difference coarray and the sum coarray. When taking the coprime array as the array model, we find that the elements of the sum coarray can fill up all the holes in the difference coarray. Besides, the sum coarray contains bonus uniform linear array (ULA) segments which extend the consecutive range of the difference coarray. As a result, the consecutive lags of the diff–sum coarray are much more than those of the difference coarray. For analysis, we derive the hole locations and consecutive ranges of the difference set and the sum set, discuss the complementarity of the two sets, and provide the analytical expression of the diff–sum virtual aperture. Simulations verify the effectivity of the improved method and show the high DOF of the diff–sum coarray.     

The application of hybrid feature based on local mean decomposition for motor imagery electroencephalogram signal classification

This paper proposed a hybrid feature extraction algorithm based on local mean decomposition (LMD), which has better solved the existing problems of low classification performance and adaptability limitation. LMD is employed to decompose the electroencephalogram (EEG) signal into multiple components, and then, the hybrid features based on instantaneous energy, fuzzy entropy, and mathematical morphological features are extracted on specific components, and the optimal feature combination is selected by analysis of variance (ANOVA). Finally, the classification result is output by the linear discriminant analysis (LDA) classifier. The results show that the maximum accuracy of the subjects in Data Set III of BCI-II by the method in this paper is 92.14%, and the maximum mutual information value is 0.8. The number of novel features used in this paper is small, and the complexity of the algorithm is reduced. It can adaptively select effective features according to individual differences and has good robustness.     

Detection and parameter estimation of multicomponent LFM signal based on the fractional fourier transform

Copyright by Science in China Press 2Linear frequency modulation (LFM or chirp) signals are widely used in information systems such as radar, sonar, and communications. In these systems, to detect and estimate LFM signals is an important problem. For a long time, various methods based on maximum likelihood estimator are the predominant solutions to this task. Most of these methods can be ascribed to a multivariable optimization algorithm and are usually computationally demanding in impleme…     

An incremental feature selection approach based on scatter matrices for classification of cancer microarray data

Microarray data are often characterized by high dimension and small sample size. There is a need to reduce its dimension for better classification performance and computational efficiency of the learning model. The minimum redundancy and maximum relevance (mRMR), which is widely explored to reduce the dimension of the data, requires discretization and setting of external parameters. We propose an incremental formulation of the trace of ratio of the scatter matrices to determine a relevant set of genes which does not involve discretization and external parameter setting. It is analytically shown that the proposed incremental formulation is computationally efficient in comparison to its batch formulation. Extensive experiments on 14 well-known available microarray cancer datasets demonstrate that the performance of the proposed method is better in comparison to the well-known mRMR method. Statistical tests also show that the proposed method is significantly better when compared to the mRMR method.     

基于容性耦合电极的可穿戴心电信号检测及其去噪算法研究

针对传统心电检测系统存在佩戴电极不方便和电极导电膏易脱水等问题,在研究分析电容耦合工作原理的基础上,设计了一种可穿戴容性耦合电极。针对这种可穿戴容性耦合电极,提出了一种改进小波阈值去噪算法,该算法结合心电信号与噪声小波系数分布特性,采用改进阈值函数对分解后小波系数量化处理并重构心电信号。利用MIT￣BIH数据库验证,该算法能有效消除心电信号中的噪声干扰,相比平滑滤波、数学形态滤波和经验模式分解信噪比提高了10.72％,均方误差减小了27.29％。心电检测实验表明可穿戴容性耦合心电信号检测系统能够准确检测出人体心电信号主要特征。     

基于Sigrity在SDIO板级信号完整性仿真分析与优化

随着高集成度集成电路与高速板级印制电路的发展,板间通信频率已经达到GHz水平,传统板级电路设计方案已经无法普及到更高频率的电路设计。针对高速SDIO总线在板级的设计,基于Cadence Sigrity平台的信号完整性仿真,提出了一种针对SDIO总线的高速信号仿真方法,该方法对SDIO总线有较高的仿真参考意义,通过海思Hi3516EV200嵌入式平台的板级电路设计与仿真优化,对层叠结构、层叠顺序、走线长度、地过孔、过孔数目实验仿真,优化PCB设计,对S参数与时域图进行研究与分析,提出了一种SDIO总线的电路走线设计参考方法,通过理论分析与仿真实验论证了该方案的可行性与实用价值,填补了信号完整性仿真分析中对SDIO总线设计的空白。     

基于热释电红外的果园防盗系统

针对传统的断线型报警器在果园防盗中布线繁琐,不易管理的问题,设计了基于热释电红外的果园防盗系统。选用了双元传感器D203S做人体探测,并经过EG4002对其信号放大滤波后通过固态继电器驱动报警装置报警。同时为防止小动物引起误报,采用了上下2片传感器输出信号逻辑与的方法。实验结果表明,探测器探测范围可达到10m,探测性能不受天气、时间与步行速度的影响,只对移动人体报警,对小动物不误报。     

Stability analysis of periodic orbit of an adaptive notch filter for frequency estimation of a periodic signal

Online frequency estimation of a sinusoidal signal is a classical problem and has many practical applications. Recently an adaptive notch filter (ANF) with global convergence property has been developed for frequency estimation of a pure sinusoidal signal. This paper addresses a modified ANF structure that can estimate the fundamental frequency of any periodic signal including pure sinusoidal signals. To prove the stability of the modified ANF, the paper introduces a new theorem that shows for any periodic signal, there exists a locally asymptotically stable periodic orbit of this ANF by which the frequency estimation becomes feasible. This alternative stability proof is simple and uses widely known mathematical tools, and therefore alleviates the problem complexity even when the input signal is a pure sinusoidal signal. A further contribution of this paper is obtaining a necessary and sufficient condition in terms of design parameters for local asymptotical stability of the modified ANF. This condition, obtained from the numerical study of Floquet multipliers of a linear time-varying periodic system, provides a strict stability region in the modified ANF design parameters space.     

Automatic classification of sleep stages based on the time-frequency image of EEG signals

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner–Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.     

Incremental learning with partial-supervision based on hierarchical Dirichlet process and the application for document classification

Hierarchical Dirichlet process (HDP) is an unsupervised method which has been widely used for topic extraction and document clustering problems. One advantage of HDP is that it has an inherent mechanism to determine the total number of clusters/topics. However, HDP has three weaknesses: (1) there is no mechanism to use known labels or incorporate expert knowledge into the learning procedure, thus precluding users from directing the learning and making the final results incomprehensible; (2) it cannot detect the categories expected by applications without expert guidance; (3) it does not automatically adjust the model parameters and structure in a changing environment. To address these weaknesses, this paper proposes an incremental learning method, with partial supervision for HDP, which enables the topic model (initially guided by partial knowledge) to incrementally adapt to the latest available information. An important contribution of this work is the application of granular computing to HDP for partial-supervision and incremental learning which results in a more controllable and interpretable model structure. These enhancements provide a more flexible approach with expert guidance for the model learning and hence results in better prediction accuracy and interpretability.     

Strategy for the classification of the level of knowledge based on a graph-analytical model

Translated from Kibernetika, No. 5, pp. 109–112, September–October,1988.