A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

A new reverse reduce-error ensemble pruning algorithm

Although greedy algorithms possess high efficiency, they often receive suboptimal solutions of the ensemble pruning problem, since their exploration areas are limited in large extent. And another marked defect of almost all the currently existing ensemble pruning algorithms, including greedy ones, consists in: they simply abandon all of the classifiers which fail in the competition of ensemble selection, causing a considerable waste of useful resources and information. Inspired by these observations, an interesting greedy Reverse Reduce-Error (RRE) pruning algorithm incorporated with the operation of subtraction is proposed in this work. The RRE algorithm makes the best of the defeated candidate networks in a way that, the Worst Single Model (WSM) is chosen, and then, its votes are subtracted from the votes made by those selected components within the pruned ensemble. The reason is because, for most cases, the WSM might make mistakes in its estimation for the test samples. And, different from the classical RE, the near-optimal solution is produced based on the pruned error of all the available sequential subensembles. Besides, the backfitting step of RE algorithm is replaced with the selection step of a WSM in RRE. Moreover, the problem of ties might be solved more naturally with RRE. Finally, soft voting approach is employed in the testing to RRE algorithm. The performances of RE and RRE algorithms, and two baseline methods, i.e., the method which selects the Best Single Model (BSM) in the initial ensemble, and the method which retains all member networks of the initial ensemble (ALL), are evaluated on seven benchmark classification tasks under different initial ensemble setups. The results of the empirical investigation show the superiority of RRE over the other three ensemble pruning algorithms.     

基于模式间混淆关系自适应构建层次化分类器

为了解决层次化分类器的设计难点——子分类器的层属关系及其内部组成的确定,本文首先定义了模式间混淆关系,用于描述不同模式在判决域中的相互作用;进而提出了基于混淆矩阵度量模式间混淆关系的方法。设计并实现了多模式混淆关系分析机MPCRAM,将有师指派和无师自组两种常用的模式重组方法有机结合,遵循Fisher准则,自适应地产生层次化分类器结构。大量综合测试证实了该方法有效、实用,可显著提高分类器的识别性能和稳健性。     

Acquisition and pattern recognition of spectrum information of welding metal transfer

With the creation of a pattern recognition system for metal transfer mode, this article has collected five kinds of spectrum signals in gas metal arc welding (MIG, MAG and CO₂) and take them as training samples. These samples have been pretreated by computer, several key characteristic parameters of the spectrum signal have been creatively extracted, and a corresponding recognition function and a minimum-distance-classifier have been constructed. The results show that the pattern recognition of several kinds of metal transfer modes for the metal gas arc welding can be done successfully, and relative important parameters in welding process, such as the frequency of droplet transfer and the approximate diameter of each droplet, can also be obtained.     

基站智能天线多波束合成技术

采用数字波束形成的技术，给出了基站智能天线多波束合成的有效算法。对圆环阵分布的基站天线多波束方向图进行了综合，得到了空间隔离度很好的多个波束，并将副瓣控制在30dB以下，结果令人满意。该方法可用于任意单元构成的不均匀阵列天线，还可用于波束展宽及扇形波束的合成。     

LEO短数据通信小卫星天线系统设计

本文主要针对 LEO小卫星低速率数据通信的应用 ,从系统的角度提出对卫星天线和地面终端天线的设计要求 ,分析了卫星天线采用赋形方向图对通信系统性能的影响     

基于Powell图的模式识别法建立化学反应速度方程

本文基于Ｐｏｗｅｌｌ图，提出了用模式识别技术中的相似系数法来确定化学反应级数的方法，并给出了计算实例。     

薄板坯连铸工艺液芯压下的有限元分析

简要介绍世界几种主要薄板坯连铸工艺。着重叙述应用大型通用商业软件MSC.MARC。模拟液芯压下时铸坯的变形，确定最佳压下方案的有限元分析，液芯压下应该在结晶器出口处及二冷区扇形段开始部分进行，这样能减小铸坯的应变。     

Pattern expressions and pattern automata

We define the pattern expressions as an extension of both regular expressions and patterns. We prove several properties of the new family of languages, similar to those of extended regex languages [Câmpeanu et al., Int. J. Found. Comput. Sci. 14 (6) (2003) 1007-1018]. We also define an automata system that recognizes these languages. Differences between regex and pattern expressions are also discussed.