关键词： 电子鼻   中草药   分类器   属性筛选  

摘要： 目的:将机器学习方法引入到电子鼻系统,应用于分类器的优化中,建立一种准确、快捷的中草药智能鉴别手段。方法:以菊科常用八味药材为载体,基于电子鼻建立中药气味特征指纹图谱,联合Best First搜索策略加Cfs Subset Eval评估策略的BC属性筛选法,并结合径向基函数、随机森林等建立分类器。采用十折交叉验证法、外部测试集验证法,展开对智能鉴别系统性能的预测与评估。结果:在传感器数量从12缩减为5的情况下,所建立的分类器仍保持原有正判率(100%),此方案简便、准确;并实现了中草药电子鼻智能鉴别系统的建立。结论:本研究首次呈现了BC属性筛选法对中草药电子鼻智能鉴别系统中分类器的优化;可望为中药智能鉴别提供一种快速、可靠而有效的分析方法。

关键词： 机器学习   量子多体物理   人工神经网络   指数墙困难  

摘要： 机器学习方法近年来在许多不同的领域受到广泛的关注,文章回顾了机器学习方法在量子多体物理中的应用的几个有代表性的例子,着重讨论了机器学习方法对于解决量子多体物理中"指数墙"困难的可能的潜在意义。除此以外,量子多体物理中的若干方法和思想反过来可能对理解机器学习领域面临的核心问题有重要的启发作用。

关键词： 机器学习   K近邻   随机森林   支持向量机   目标分类  

摘要： 对K近邻和随机森林学习方法进行改进,提出一种融合的机器学习方法。通过计算待分类特征与训练库中各个类中心之间的距离,进行最近邻分类,增强K近邻学习方法的鲁棒性,提高其运算效率;通过随机划分将改进KNN分类器的多元输出转化为二元输出,用其构建随机森林中各个决策结点的决策函数,降低各决策结点数据的错分率,提高随机森林学习方法的目标分类正确率。实验结果表明,该方法对手写数字目标的分类正确率高于经典的K近邻、Adaboost、支持向量机和随机森林学习方法。

关键词： Annual reports   Financial statement fraud   Text mining   Feature selection   Machine learning  

摘要： Financial statement fraud has been serious concern for investors, audit firms, government regulators, and other capital market stakeholders. Intelligent financial statement fraud detection systems have therefore been developed to support decision-making of the stakeholders. Fraudulent misrepresentation of financial statements in managerial comments has been noticed in recent studies. As such, the purpose of this study was to examine whether an improved financial fraud detection system could be developed by combining specific features derived from financial information and managerial comments in corporate annual reports. To develop this system, we employed both intelligent feature selection and classification using a wide range of machine learning methods. We found that ensemble methods outperformed the remaining methods in terms of true positive rate (fraudulent firms correctly classified as fraudulent). In contrast, Bayesian belief networks (BBN) performed best on non-fraudulent firms (true negative rate). This finding is important because interpretable "green flag" values (for which fraud is likely absent) could be derived, providing potential decision support to auditors during client selection or audit planning. We also observe that both financial statements and text in annual reports can be utilised to detect non-fraudulent firms. However, non-annual report data (analysts' forecasts of revenues and earnings) are necessary to detect fraudulent firms. This finding has important implications for selecting variables when developing early warning systems of financial statement fraud. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Malus   phenolic compounds   sugars   organic acids   amygdalin   predictive modeling  

摘要： The present study represents the first major attempt to characterize the biochemical profile in different tissues of a large selection of apple cultivars sourced from the United Kingdom's National Fruit Collection comprising dessert, ornamental, cider, and culinary apples. Furthermore, advanced machine learning methods were applied with the objective to identify whether the phenolic and sugar composition of an apple cultivar could be used as a biomarker fingerprint to differentiate between heritage and mainstream commercial cultivars as well as govern the separation among primary usage groups and harvest season. A prediction accuracy of >90% was achieved with the random forest method for all three models. The results highlighted the extraordinary phytochemical potency and unique profile of some heritage, cider, and ornamental apple cultivars, especially in comparison to more mainstream apple cultivars. Therefore, these findings could guide future cultivar selection on the basis of health-promoting phytochemical content.

关键词： MACHINE learning   FOREST surveys   AIRBORNE lasers  

摘要： Since Equation (2) has been rearranged incorrectly during preparation for this article [1], the authors would like to correct the relevant text of Section 3.4.3 as follows:

关键词： 不均衡数据   混合模型   多标记学习   特征选择  

摘要： 数字化经络仪、中医健康量表和四诊仪是中医临床常用辅助诊断工具,提供了很多中医临床数据。数据分布不均衡,同一个病例具有多个诊断标记是临床数据常见现象。以亚健康数据为例探索针对不均衡数据的机器学习分类方法;以肾脏疾病为例研究综合三种辅助诊断工具的混合分类模型;以心血管病、血脂异常疾病、尿酸升高类疾病为例,探索多标记数据分类方法。实验均取得良好分类效果,同时所选择特征符合医学理论,具有临床指导意义。

关键词： 电子病历   机器学习   通用框架软件   支持向量机  

摘要： 本文利用文本工程通用框架软件的XML配置文件,来指定所学文档使用的特征参数、学习算法,实现文本病历医学短语抽取的机器学习。结果计算机能很方便的在大段病程资料中快速自动获取医生所需的医学短语信息。本学习算法具有较好的实用性,达到了预期要求。

关键词： conotoxin   superfamily   ion channel   machine learning method  

摘要： Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset;(ii) strategies for extracting sequence features;(iii) feature selection techniques;(iv) machine learning methods for classifying conotoxins;(v) the results obtained by these methods and the published tools;and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

关键词： HIV-1 integrase (IN) LEDGF   p75 inhibitor   Classification model   Machine learning method   Extended connectivity fingerprints (ECFP_4)  

摘要： HIV-1 integrase (IN) is a promising target for anti-AIDS therapy, and LEDGF/p75 is proved to enhance the HIV-1 integrase strand transfer activity invitro. Blocking the interaction between IN and LEDGF/p75 is an effective way to inhibit HIV replication infection. In this work, 274 LEDGF/p75-IN inhibitors were collected as the dataset. Support Vector Machine (SVM), Decision Tree (DT), Function Tree (FT) and Random Forest (RF) were applied to build several computational models for predicting whether a compound is an active or weakly active LEDGF/p75-IN inhibitor. Each compound is represented by MACCS fingerprints and CORINA Symphony descriptors. The prediction accuracies for the test sets of all the models are over 70%. The best model Model 3B built by FT obtained a prediction accuracy and a Matthews Correlation Coefficient (MCC) of 81.08% and 0.62 on test set, respectively. We found that the hydrogen bond and hydrophobic interactions are important for the bioactivity of an inhibitor.