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.     

Ecosystem mapping at the African continent scale using a hybrid clustering approach based on 1-km resolution multi-annual data from SPOT/VEGETATION

The goal of this study is to propose a new classification of African ecosystems based on an 8-year analysis of Normalized Difference Vegetation Index (NDVI) data sets from SPOT/VEGETATION. We develop two methods of classification. The first method is obtained from a k-nearest neighbour (k-NN) classifier, which represents a simple machine learning algorithm in pattern recognition. The second method is hybrid in that it combines k-NN clustering, hierarchical principles and the Fast Fourier Transform (FFT). The nomenclature of the two classifications relies on three levels of vegetation structural categories based on the Land Cover Classification System (LCCS). The two main outcomes are: (i) The delineation of the spatial distribution of ecosystems into five bioclimatic ecoregions at the African continental scale; (ii) Two ecosystem maps were made sequentially: an initial map with 92 ecosystems from the k-NN, plus a deduced hybrid classification with 73 classes, which better reflects the bio-geographical patterns. The inclusion of bioclimatic information and successive k-NN clustering elements helps to enhance the discrimination of ecosystems. Adopting this hybrid approach makes the ecosystem identification and labelling more flexible and more accurate in comparison to straightforward methods of classification. The validation of the hybrid classification, conducted by crossing-comparisons with validated continental maps, displayed a mapping accuracy of 54% to 61%.     

基于可传递信度模型的自适应k-NN分类器

针对训练模式类标签不精确的识别问题,提出了基于可传递信度模型(TBM)的自适应k-NN分类器,它通过运用pignistic变换,可以方便地对待识别模式真正所属的类做出决策,并通过梯度下降来最小化训练模式的输出类标签与目标类标签之间的误差函数,以实现参数的自适应学习.实验表明,该分类器用于处理训练模式类标签不精确的模式识别问题是有效的,且与参数优化前的基于TBM的k-NN分类器相比,其误分类率更低、鲁棒性更强.     

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.     

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。     

一种新的加权k-最临近分类方法

k-最临近(k-NN)分类方法在计算两训练样本的相异度时给每一属性加相同的权,这样会造成分类的准确性下降,尤其当存在很多无关属性时,甚至会造成混乱。针对这一弱点该文提出了一种用每一属性的信息增益作为该属性的权来计算训练样本间的相异度的数学模型,并将这一模型应用于k-最临近分类方法,改善了该方法的分类质量。     

Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems

This paper describes applications of non-parametric and parametric methods for estimating forest growing stock volume using Landsat images on the basis of data measured in the field, integrated with ancillary information. Several k-Nearest Neighbors (k-NN) algorithm configurations were tested in two study areas in Italy belonging to Mediterranean and Alpine ecosystems. Field data were acquired by the regional forest inventory and forest management plans, and satellite images are from Landsat 5 TM and Landsat 7 ETM+. The paper describes the data used, the methodologies adopted and the results achieved in terms of pixel level accuracy of forest growing stock volume estimates. The results show that several factors affect estimation accuracy when using the k-NN method. For the two test areas a total of 3500 different configurations of the k-NN algorithm were systematically tested by changing the number and type of spectral and ancillary input variables, type of multidimensional distance measures, number of nearest neighbors and methods for spectral feature extraction using the leave-one-out (LOO) procedure. The best k-NN configurations were then used for pixel level estimation; the accuracy was estimated with a bootstrapping procedure; and the results were compared to estimates obtained using parametric regression methods implemented on the same data set.

The best k-NN growing stock volume pixel level estimates in the Alpine area have a Root Mean Square Error (RMSE) ranging between 74 and 96 m³ ha^− 1 (respectively, 22% and 28% of the mean measured value) and between 106 and 135 m³ ha^− 1 (respectively, 44% and 63% of the mean measured value) in the Mediterranean area. On the whole, the results cast a promising light on the use of non-parametric techniques for forest attribute estimation and mapping with accuracy high enough to support forest planning activities in such complex landscapes. The results of the LOO analyses also highlight the importance of a local empirical optimization phase of the k-NN procedure before defining the best algorithm configuration. In the tests performed the pixel level accuracy increased, depending on the k-NN configuration, as much as 100%.     

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.