Intrauterine growth restriction (IUGR) risk decision based on support vector machines

This paper studies the risk of intrauterine compromise in the fetuses with intrauterine growth restriction (IUGR) using support vector machines (SVM). A structured and globally optimized SVM system may be preferable procedure in the identification of IUGR fetus at risk. The IUGR risk is estimated in two stages: In the first stage, noninvasive Doppler pulsatility index (PI) and resistance index (RI) of umbilical artery (UA), middle cerebral artery (MCA) and ductus venosus (DV), and amniotic fluid index (AFI) are retrospectively analyzed and the Doppler indices are applied to the SVM system to make a diagnosis decision on the fetal well being as “reactive” or “nonreactive and/or fetal distress (FD)” on the nonstress test (NST) (training data). In the second stage (testing data), the decision is validated by the NST (target value). Experiments are performed in retrospective clinical situation. Forty-four preterm with IUGR and without IUGR pregnancies before 34 weeks gestation are considered. Also, the nonparametric Bayes-risk decision rule, k-nearest neighbor (k-NN), is used for comparison. It is observed that the SVM system is proven to be useful in predicting the expected risk in IUGR cases in the small population study. Also, the PI and RI values of UA, MCA and DV are effective in distinguishing IUGR cases at risk.     

基于矢量量化的快速图像检索

传统索引方法对高维数据存在"维数灾难"的困难.而对数据分布的精确描述及对数据空间的有效划分是高维索引机制中的关键问题.提出一种基于矢量量化的索引方法.该方法使用高斯混合模型描述数据的整体分布,并训练优化的矢量量化器划分数据空间.高斯混合模型能更好地描述真实图像库的数据分布;而矢量量化的划分方法可以充分利用维之间的统计相关性,能够对数据向量构造出更加精确的近似表示,从而提高索引结构的过滤效率并减少需要访问的数据向量.在大容量真实图像库上的实验表明,该方法显著减少了支配检索时间的I/O开销,提高了索引性能.     

超文本协调分类器的性能研究

为了获得高效的超文本分类算法,提出了一种新的协调分类超文本算法,并将ｋ－ＮＮ,Ｂａｙｅｓ和文档相似性引入了超文本分类领域,且这对３种分类器的超的分类效果进行了实验比较,最终得出一个高效的超文本分类器,目前,该方法已应用于新开发的两个实验系统;智能搜索引擎系统ＷｅｂＳｅａｒｃｈ和智能软件助理ＷｅｂＳｏｆｔ。     

基于Boosting的代价敏感软件缺陷预测方法

Boosting重抽样是常用的扩充小样本数据集的方法,首先针对抽样过程中存在的维数灾难现象,提出随机属性子集选择方法以进行降维处理;进而针对软件缺陷预测对于漏报与误报的惩罚因子不同的特点,在属性选择过程中添加代价敏感算法。以多个基本k-NN预测器为弱学习器,以代价最小为属性删除原则,得到当前抽样集的k值与属性子集的预测器集合,采用代价敏感的权重更新机制对抽样过程中的不同数据实例赋予相应权值,由所有预测器集合构成自适应的集成k-NN强学习器并建立软件缺陷预测模型。基于NASA数据集的实验结果表明,在小样本情况下,基于Boosting的代价敏感软件缺陷预测方法预测的漏报率有较大程度降低,误报率有一定程度增加,整体性能优于原来的Boosting集成预测方法。     

新的基于簇划分文本分类方法

基于向量空间模型的文本分类由于文本向量维数较高导致分类器效率较低.针对这一不足,提出一种新的基于簇划分的文本分类方法.其主要思想是根据向量空间中向量间的距离,将训练文档分成若干簇,同一簇中的文档具有相同类别.测试时,根据测试文档落入哪个簇,确定文档的类别,并且和传统的文本分类方法k-NN进行了比较.实验结果表明,该方法在高维空间具有良好的泛化能力和很好的时间性能.     

基于多尺度信息熵的雷达辐射源信号识别

随着雷达信号的日益复杂,从实数序列中提取特征变得越来越困难,但当它们表示成符号序列时,通常能更容易地挖掘出有效的特征参数。因此,该文提出一种基于多尺度信息熵(MSIE)的雷达信号识别方法。首先通过符号聚合近似(SAX)算法在不同字符集尺度下将雷达信号转换为符号化序列;然后联合各符号序列的信息熵值,组成MSIE特征向量;最后,使用k邻近算法(k-NN)作为分类器实现雷达信号的分类识别。通过仿真6种典型的雷达信号进行验证,结果表明该方法在信噪比(SNR)为5 dB时,不同雷达信号的识别正确率大于90%,并且优于传统的基于复杂度特征(盒维数和稀疏性)的识别方法。

     

基于Bagging的组合k-NN预测模型与方法

k-近邻方法基于单一k值预测,无法兼顾不同实例可能存在的特征差异,总体预测精度难以保证.针对该问题,提出了一种基于Bagging的组合k-NN预测模型,并在此基础上实现了具有属性选择的Bgk-NN预测方法.该方法通过训练建立个性化预测模型集合,各模型独立生成未知实例预测值,并以各预测值的中位数作为组合预测结果.Bgk-NN预测可适用于包含离散值属性及连续值属性的各种类型数据集.标准数据集上的实验表明,Bgk-NN预测精度较之传统k-NN方法有了明显提高.     

基于卡尔曼滤波与k-NN算法的可穿戴跌倒检测技术研究

针对老年人跌倒检测的准确性和实时性需求,该文首先建立了基于姿态角的活动描述模型,研发了集成加速度传感器、陀螺仪和蓝牙的活动感知模块,从而实时采集运动变化数据并使用蓝牙发送到智能手机。其次,选取姿态角及加速度信号向量模作为特征量,通过卡尔曼滤波对数据进行去噪与融合,并应用滑动窗口和k-NN算法实现了可实时感知老年人跌倒并报警的系统。实验证明系统在二分类场景下的跌倒检测准确率为98.9%,而敏感度和特异性分别达到98.9%和98.5%,验证了系统具有良好的实时性和较高的准确率。     

Continually Answering Constraint k-NN Queries in Unstructured P2P Systems

We consider the problem of efficiently computing distributed geographical k-NN queries in an unstructured peer-to-peer (P2P) system,in which each peer is managed by an individual organization and can only communicate with its logical neighboring peers.Such queries are based on local filter query statistics,and require as less communication cost as possible,which makes it more difficult than the existing distributed k-NN queries.Especially,we hope to reduce candidate peers and degrade communication cost.In this paper,we propose an efficient pruning technique to minimize the number of candidate peers to be processed to answer the k-NN queries.Our approach is especially suitable for continuous k-NN queries when updating peers,including changing ranges of peers,dynamically leaving or joining peers,and updating data in a peer. In addition,simulation results show that the proposed approach outperforms the existing Minimum Bounding Rectangle (MBR.)-based query approaches,especially for continuous queries.     

A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases

The use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems has improved in a great deal to help medical experts in diagnosing. In this paper, we investigate the performance of an artificial immune system (AIS) based fuzzy k-NN algorithm to determine the heart valve disorders from the Doppler heart sounds. The proposed methodology is composed of three stages. The first stage is the pre-processing stage. The feature extraction is the second stage. During feature extraction stage, Wavelet transforms and short time Fourier transform were used. As next step, wavelet entropy was applied to these features. In the classification stage, AIS based fuzzy k-NN algorithm is used. To compute the correct classification rate of proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters. 95.9% sensitivity and 96% specificity rate was obtained.