Application of BT and PC-BT in Homo sapiens gene prediction

Digital signal processing is gaining popularity in gene classification and prediction. Spectral content of gene is estimated using Blackman–Tukey (BT) method for identification of Homo sapiens cancer genes. A data reduction method; principal component analysis (PCA) is used prior to Blackman–Tukey method (PC-BT) for better identification of genes. Selection of proper model order is prime important in PCA method for considering the number of principal components (PCs) and the performance of the method depends on optimal selection of PCs. Cumulative percent variance and scree test residual percent variance are used as measurement metric for selection of PCs. Quality factor is used to judge the performance of the estimators. Simulation results show the clarity of spectrum plot in PC-BT method is 200 % higher than BT method. The spectral peaks observed in cancer genes are used to screen out cancer genes from healthy genes. The methods are successfully tested on breast, prostate and colon Homo sapiens. Healthy and cancer Homo sapiens genes are downloaded from Cancer Genome Anatomy Project (CGAP) site and National Centre for Biotechnology Information (NCBI) site.     

在超声检测中使用DSP实现最大熵谱估计

最大熵谱估计法与传统的谱估计法比较具有高分辨率、谱线平滑、适用于短数据序列等优点,但是分辨率越高计算的工作量就越大,为了解决分辨率和计算速度二者的矛盾,本文在粘接质量超声检测中使用DSP实现最大熵谱估计,在保证高分辨率的情况下有效地提高了处理速度.     

一种基于模糊熵的模糊分类算法

在模糊ID3算法中,用模糊分类熵选择扩展属性,以自顶向下的方式递归地构建模糊决策树,对数据进行分类。提出了一种基于属性模糊熵的模糊分类算法,不同于模糊ID3算法,模糊条件属性的模糊熵作为权值用来对相对模糊频率进行加权,综合考虑各个模糊条件属性对分类的贡献。实例分析和实验结果表明了这一算法的有效性。     

基于Copula熵的互信息估计方法

互信息是一种常用的衡量变量相关性的方法,但在互信息估计过程中,联合概率密度的估计往往十分困难.为了避免联合概率密度的估计,同时有效提高互信息估计的准确度与效率,本文提出一种基于Copula熵的互信息估计方法.利用Copula熵与互信息之间的关系,将互信息的估计转化为对Copula熵值的估计.采用基于Kendall秩相关系数的参数估计方法对Copula函数的参数进行估计.所提算法分别与直方图法、核方法、κ近邻法和极大似然法进行比较.二维高斯数据上的仿真结果表明,所提方法能够快速准确地对互信息值进行估计.     

Evolution and development of brain networks: from Caenorhabditis elegans to Homo sapiens

Neural networks show a progressive increase in complexity during the time course of evolution. From diffuse nerve nets in Cnidaria to modular, hierarchical systems in macaque and humans, there is a gradual shift from simple processes involving a limited amount of tasks and modalities to complex functional and behavioral processing integrating different kinds of information from highly specialized tissue. However, studies in a range of species suggest that fundamental similarities, in spatial and topological features as well as in developmental mechanisms for network formation, are retained across evolution. 'Small-world' topology and highly connected regions (hubs) are prevalent across the evolutionary scale, ensuring efficient processing and resilience to internal (e.g. lesions) and external (e.g. environment) changes. Furthermore, in most species, even the establishment of hubs, long-range connections linking distant components, and a modular organization, relies on similar mechanisms. In conclusion, evolutionary divergence leads to greater complexity while following essential developmental constraints.     

Sequential estimation and identification of reflection coefficients by minimax entropy inverse filtering

The problem of inverse filtering is formulated as one of finding an optimal joint probability density function for a given sequence of observations. Using the joint entropy of N consecutive observations as a performance index, this paper develops a minimax entropy approach for sequentially estimating the reflection coefficients of a stationary random process from a short observation interval. For the second order discrete-time processes the resulting algorithm has a lattice type digital ladder structure which is quite desirable for its high computational efficiency, low error sensitivity, and stability. The algorithm can also be implemented in a parallel representation. A 4th order linear model example considered here shows excellent identification accuracy, particularly of the frequency peaks. Also, a procedure is described for determining the dimension of the best fitting linear model subject to a fidelity criterion.     

Information entropy for ordinal classification

Ordinal classification plays an important role in various decision making tasks.However, little attention is paid to this type of learning tasks compared with general classification learning.Shannon information entropy and the derived measure of mutual information play a fundamental role in a number of learning algorithms including feature evaluation, selection and decision tree construction.These measures are not applicable to ordinal classification for they cannot characterize the consistency of monotonic...     

基于强度熵的中文关键词识别方法

文本的关键词识别是文本挖掘中的基本问题之一。在研究现有基于复杂网络的关键词识别方法的基础上,从整个复杂网络拓扑结构特征的信息缺失角度来考察各节点的重要程度。提出强度熵测度来量化评估各节点重要程度,用于解决中文关键词识别问题。实验结果表明,该评估方法简单有效,特别适用于带权复杂网络的节点重要性评估。     

基于最大信息熵模型的异常流量分类方法*

最大信息熵原理已被成功地应用于各种自然语言处理领域,如机器翻译、语音识别和文本自动分类等,提出了将其应用于互联网异常流量的分类。由于最大信息熵模型利用二值特征函数来表达和处理符号特征,而KDD99数据集中存在多种连续型特征,因此采用基于信息熵的离散化方法对数据集进行预处理,并利用CFS算法选择合适的特征子集,形成训练数据集合。最后利用BLVM算法进行参数估计,得到满足最大熵约束的指数形式的概率模型。通过实验,比较了最大信息熵模型和Naive Bayes、Bayes Net、SVM与C4.5决策树方法之间的精度、召回率、F-Measure,发现最大信息熵模型具有良好的综合性能,尤其在训练数据集样本数量有限的情况下仍然能保持较高的分类精度,在实际应用中具有广阔的前景。     

Linear metrics for entropy estimation

An entropy estimator constructed with respect to specially selected metrics is studied. It is shown that the estimator converges almost everywhere and the decrease in its variance is of an power order. For symmetric Bernoulli measures, the bias of the estimator is found.     

基于脑电微分熵的警觉度估计方法

为提高人员警觉度实时估计精度,提出了一种基于微分熵（DE）、改进滑动平均和双向二维主成分分析（TD-2DPCA）的警觉度实时估计方法。首先将总频带以某一带宽分解为多个子频带,在每个子频带提取DE;然后结合警觉度的时间动态特征对传统滑动平均方法进行改进,利用改进滑动平均对DE进行了平滑处理;之后利用TD-2DPCA对DE进行降维,并采用最小二乘支持向量机（LS-SVM）建立特征矩阵与警觉度之间的回归模型,以实现警觉度的实时准确估计。最后利用SEED-VIG数据集进行了实验验证,结果表明,采用改进滑动平均和TD-2DPCA方法对数据进行处理可以提高警觉度估计的精度并降低估计时间;总频带在0~35 Hz以内,分解带宽为1 Hz或2 Hz时,提取的DE进行警觉度估计能够达到最高的估计精度;Pearson相关系数约为0.91,RMSE约为0.09,优于现有的警觉度估计方法。     

A DSP based frequency domain approach for classification of transmission line faults

This paper employs a digital signal processing (DSP) based frequency domain approach using wavelet multi-resolution analysis (MRA) to overcome difficulties such as fault inception angle, fault impedance and fault distance associated with conventional time domain approach employing voltage and current based measurements for fault classification in case of digital relaying of transmission line. The frequency domain approach for fault classification algorithm uses wavelet MRA technique to extract the features of the current signals based on harmonics generated at the instant of occurrence of fault due to abrupt change of currents in a three phase transmission line. Since choice of particular wavelet plays a vital role for extracting features of generated harmonics, therefore an attempt has been made in this proposed research to extensively investigate using 16 wavelets to establish the superiority of Db4 wavelet over other standard wavelets for accurate fault classification.     

基于IMF能量熵的目标特征提取与分类方法

提出了一种基于固有模态函数(IMF)能量熵的特征提取与选择方法。对三类信号进行了经验模态分解(EMD),得到IMF。对于不同类别的信号,同阶的IMF能量有明显的不同。选择IMF能量作为特征向量,并选判别熵作为分类判据,同时给出了两种能量熵的计算公式。采用K-近邻分类器对三类信号进行了分类试验,试验结果表明,基于最佳特征向量选择的分类试验的平均正确识别率达80%以上。     

Feature subset selection based on fuzzy entropy measures for handling classification problems

In this paper, we present a new method for dealing with feature subset selection based on fuzzy entropy measures for handling classification problems. First, we discretize numeric features to construct the membership function of each fuzzy set of a feature. Then, we select the feature subset based on the proposed fuzzy entropy measure focusing on boundary samples. The proposed method can select relevant features to get higher average classification accuracy rates than the ones selected by the MIFS method (Battiti, R. in IEEE Trans. Neural Netw. 5(4):537–550, 1994), the FQI method (De, R.K., et al. in Neural Netw. 12(10):1429–1455, 1999), the OFEI method, Dong-and-Kothari’s method (Dong, M., Kothari, R. in Pattern Recognit. Lett. 24(9):1215–1225, 2003) and the OFFSS method (Tsang, E.C.C., et al. in IEEE Trans. Fuzzy Syst. 11(2):202–213, 2003).

基于最大熵模型的汉语短语间停顿识别

正确标记短语间的停顿,对提高文语转换系统合成语音的自然度起着重要作用。介绍一种采用最大熵模型从真实自然的语音流中自动识别汉语短语间停顿的方法。模型的特征集包含语音和词法两类特征,采用半自动的方式获得。首先由人工根据经验设计候选特征集,然后采用特征选择算法对候选特征进行筛选,选择更有效的特征构成最终特征集,并训练生成用于汉语短语间停顿识别的最大熵模型。3组实验的结果表明,模型能够取得比较满意的短语间停顿识别效果。     

基于最大熵原理的Web服务QoS概率分布获取

为了进行服务风险管理,需要了解服务质量（QoS）的随机特性,而描述QoS随机特性的一种有效手段是获得其准确的概率分布。为此,提出了一种基于最大熵原理在小样本情况下获取Web服务QoS概率分布的方法。方法采用最大熵原理将小样本情况下QoS概率分布获取的问题规约为一个由已知QoS数据确定约束条件的最优化问题进行求解,获得QoS概率密度函数的解析式,然后设计了对该概率密度函数解析式参数进行估计的算法。最后,以实际的Web服务QoS数据为基础,通过实验验证了该方法对不同QoS分布获取时的有效性和合理性,并验证了分布获取算法的效率和终止性。     

基于块特征分类的运动估计算法

运动估计作为H.264中最重要最耗时的部分,大量的算法研究都是通过减少搜索点数来降低计算量。视频序列中,人们感兴趣的往往是运动的物体,背景部分一般拥有大量的静止块,帧间背景间的运动可以认为完全是由摄像机的运动引起的。利用背景块与前景块的图像特征结合MV预测提出一种新算法。该算法包括以下策略：静止块判断、背景块、前景块和边缘块的检测、起点预测和搜索窗口的改进,同时引进了边缘检测技术。实验结果表明,该算法与全搜索算法(FS)、UMHex、SUMHex相比,搜索时间减少了,而搜索精度接近FS, 码率只有很小的增加。     

基于子区域分类的乳腺密度估计

乳腺密度常用于乳腺癌早期诊断。提出了一种基于子区域分析的乳腺密度估计方法。该方法先将整幅钼靶X线图像中的乳腺区域分割为互不重叠的子区域,采用直方图矩描述各子区域的灰度分布,并结合支持向量机将各子区域分为高密度和低密度两类;通过计算高密度子区域占所有子区域的比例,最终得到钼靶图像中乳腺密度。实验表明,该方法对乳腺X线图像具有很好的分类效果。     

基于分类差异与信息熵对抗的无监督域适应算法

采用对抗训练的方式成为域适应算法的主流,通过域分类器将源域和目标域的特征分布对齐,减小不同域之间的特征分布差异。但是,现有的域适应方法仅将不同域数据之间的距离缩小,而没有考虑目标域数据分布与决策边界之间的关系,这会降低目标域内不同类别的特征的域内可区分性。针对现有方法的缺点,提出一种基于分类差异与信息熵对抗的无监督域适应算法(adversarial training on classification discrepancy and information entropy for unsupervised domain adaptation, ACDIE)。该算法利用两个分类器之间的不一致性对齐域间差异,同时利用最小化信息熵的方式降低不确定性,使目标域特征远离决策边界,提高了不同类别的可区分性。在数字标识数据集和Office-31数据集上的实验结果表明,ACDIE算法可以学习到更优的特征表示,域适应分类准确率有明显提高。