关键词： Breast cancer   Computer-aided diagnosis   Computerized features   Breast Imaging Reporting and Data System   BI-RADS   Machine learning   Receiver operating characteristic   Tissue characterization   Tumor classification   Ultrasonic imaging  

摘要： This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up'' approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy;the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions. (E-mail: jshan@***) (C) 2016 World Federation for Ultrasound in Medicine & Biology.

关键词： Canadian Prairies   Crop yield forecasting   Remote sensing   MODIS   NDVI   EVI   Machine learning   Artificial neural networks  

摘要： Crop yield forecast models for barley, canola and spring wheat grown on the Canadian Prairies were developed using vegetation indices derived from satellite data and machine learning methods. Hierarchical clustering was used to group the crop yield data from 40 Census Agricultural Regions (CARs) into several larger regions for building the forecast models. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from the Moderate-resolution Imaging Spectroradiometer (MODIS), and NDVI derived from the Advanced Very High Resolution Radiometer (AVHRR) were considered as predictors for crop yields. Multiple linear regression (MLR) and two nonlinear machine learning models - Bayesian neural networks (BNN) and model-based recursive partitioning (MOB) - were used to forecast crop yields, with various combinations of MODIS-NDVI, MODIS-EVI and NOAA-NDVI as predictors. Crop yield forecasts made using predictors from July and earlier were evaluated by the cross validated mean absolute error skill score (in reference to climatological forecasts) during 2000-2011. While MODIS-NDVI was found to be the most effective predictor for all three crops, having MODIS-EVI as an additional predictor enhanced the forecast skills. While MLR, BNN and MOB all showed significantly higher skills than climatological forecasts for all three crops, barley was the only case where the nonlinear BNN and MOB models showed slightly higher skills than MLR. The lack of skill improvement by nonlinear models over MLR is likely due to the short (12 years) record available for MODIS data, which limits our study to 2000-2011, with very low yields coming from a single severe drought year (2002). (C) 2015 Elsevier B.V. All rights reserved.

关键词： Wind power   Variational mode decomposition   Combined forecasting model   Self-adaptive multi-strategy differential evolution   Probabilistic interval prediction   Quantile regression averaging  

摘要： Due to the increasingly significant energy crisis nowadays, the exploitation and utilization of new clean energy gains more and more attention. As an important category of renewable energy, wind power generation has become the most rapidly growing renewable energy in China. However, the intermittency and volatility of wind power has restricted the large-scale integration of wind turbines into power systems. High-precision wind power forecasting is an effective measure to alleviate the negative influence of wind power generation on the power systems. In this paper, a novel combined model is proposed to improve the prediction performance for the short-term wind power forecasting. Variational mode decomposition is firstly adopted to handle the instability of the raw wind power series, and the subseries can be reconstructed by measuring sample entropy of the decomposed modes. Then the base models can be established for each subseries respectively. On this basis, the combined model is developed based on the optimal virtual prediction scheme, the weight matrix of which is dynamically adjusted by a self adaptive multi-strategy differential evolution algorithm. Besides, a probabilistic interval prediction model based on quantile regression averaging and variational mode decomposition-based hybrid models is presented to quantify the potential risks of the wind power series. The simulation results indicate that: (1) the normalized mean absolute errors of the proposed combined model from one-step to three-step forecasting are 4.34%, 6.49% and 7.76%, respectively, which are much lower than those of the base models and the hybrid models based on the signal decomposition techniques;(2) the interval forecasting model proposed can provide reliable and excellent prediction results for a certain expectation probability, which is an effective and reliable tool for the short-term wind power probabilistic interval prediction. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： 机器学习   无线信道   决策树模型   区域划分  

摘要： 本文运用机器学习方法,对无线信道的特征建立了相应的决策树分类模型。并且对所建立的决策树模型运用真实信道数据进行了测试检验,发现分类效果较好。因此,该机器学习模型对无线信道特征的识别具有较高的准确性。进而可以运用该模型对无线信道数据进行有效的区域划分,并且该模型还具备了一定的统计学意义。

关键词： Hypervelocity impact   Artificial neural network   Support vector machine   Terminal ballistics  

摘要： The outcome of projectile impacts on spaced aluminium armour (i.e. Whipple shield) at hypervelocity is traditionally predicted by semi-analytical equations known as ballistic limit equations (BLEs). For spherical aluminium projectiles impacting aluminium Whipple shields, the state-of-the-art BLE has been found to correctly reproduce the perforation/non-perforation result of approximately 75% of tests contained within a database with over 1100 entries. For such high-dimensional, complex problems, machine learning methods may be a superior approach over such semi-analytical/empirical methods. Towards this end an artificial neural network (ANN) and support vector machine (SVM) have been developed to better classify the outcome of hypervelocity impact events on aluminium Whipple shields. The ANN was found to correctly reproduce the perforation/non-perforation result of over 93% of the training exemplars, generalizing at 92.2% when applied with an optimized architecture. The SVM was not able to reach comparable levels of generalization error as the ANN, peaking at 83% after the removal of a small number of test exemplars which induced conflict within the SVM pattern recognition. Although providing a significant improvement in predictive accuracy over the conventional BLE, the performance of both the ANN and SVM were highly sensitive to the population density of the parameter space described by the training data. Within highly populated regions the qualitative trends of the machines are demonstrated to accurately identify patterns associated with projectile fragmentation and melting, while in sparsely populated regions the accuracy significantly decreases and the qualitative trends can become nonsensical. Further improvements in accuracy are limited by the inhomogeneous sampling of the parameter space defined by the current impact test database a problem expected to be typical of any terminal ballistics problem in which the cost of performing experiments limits their scop

关键词： 上证综合指数   机器学习   随机森林   支持向量机  

摘要： 上证综合指数是广大投资者关注的重要指数。上证综合指数不仅反映了我国股票市场的基本状况,同时对我国经济走向也具有重要的导向作用。对上证综合指数的预测分析以及趋势研判对稳定市场、引导投资者具有重大意义。而股票市场数据是典型的非线性系统,传统统计学预测方法在处理时预测精度较低。本文综合运用R软件并结合目前机器学习领域最新的六种方法——决策树、boosting、bagging、随机森林、支持向量机、神经网络分别对训练集进行训练,得到相应模型。并建立相应的十折交叉验证集计算出每种方法的预测均方误差进行对比。筛选出效果较好的模型,并对预测数据与真实数据进行数据可视化对比。对结果分析可知,随机森林、支持向量机两种机器学习方法拟合效果较好,且精度高。

关键词： Ingredient combinations   Recipe completion   Non-negative matrix factorization   Two-step regularized least squares   Recommender systems  

摘要： Background: Completing recipes is a non-trivial task, as the success of ingredient combinations depends on a multitude of factors such as taste, smell and texture. Scope and approach: In this article, we illustrate that machine learning methods can be applied for this purpose. Non-negative matrix factorization and two-step regularized least squares are presented as two alternative methods and their ability to build models to complete recipes is evaluated. The former method exploits information captured in existing recipes to complete a recipe, while the latter one is able to also incorporate information on flavor profiles of ingredients. The performance of the resulting models is evaluated on real-life data. Key findings and conclusions: The two machine learning methods can be used to build models to complete a recipe. Both models are able to retrieve an eliminated ingredient from a recipe and the two-step RLS model is also capable of completing an ingredient set to create a complete recipe. By applying machine learning methods on existing recipes, it is not necessary to model the complexity of good ingredient combinations to be able to complete a recipe. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： 药物靶标预测技术   中药网络药理学   基于配体的预测方法   基于靶标的预测方法   机器学习方法  

摘要： 近年来,随着网络药理学逐渐兴起,特别是"网络靶标"概念的提出,引发了中医药新的研究热潮。其所强调的整体性与系统性和中医药理论不谋而合,能够很好的解释中医药的深刻内涵,包括中药作用机制解释,药效物质的寻找,君臣佐使配伍机制阐释等,更加适合中医药的学术特点和实际。而如何高效快速地识别大量的药物与靶标蛋白间的相互作用则成为中药网络药理学研究中的一个关键问题。药物-靶标相互预测技术作为一种高效而高通量的手段,能够减少成本,快速预测成分靶标,为中药网络药理学的应用奠定基础。事实上,针对庞大的化合物和靶标数据库,不同的预测方法和技术都被开发出来,并用于预测药物和靶标的关系,且已经有许多虚拟筛选技术成功的应用于网络药理学中。根据预测原理的不同,药物-靶标相互预测技术和策略大致可以分为4类,即基于配体的预测方法,基于靶标的预测方法,机器学习方法和组合应用的预测方法。该文就药物靶标预测方法及其在中药网络药理学的应用现状进行较系统的阐述,希望能够为广大中药研究者提供有益的参考和借鉴。

关键词： 克隆代码   机器学习   预测   重构  

摘要： 克隆代码作为软件维护中的研究热点,如今的研究中心逐渐转移到克隆管理方面,克隆重构是克隆管理的研究重点,结合机器学习方法在预测方面的应用能够实现克隆重构性预测。笔者选用多种机器学习方法进行比较,试验发现贝叶斯网络在预测克隆代码可重构性过程中具有较高效率。

关键词： 机器学习方法   分类预测   启发式   错误定位  

摘要： 本文利用机器学习的方法通过训练学习,对通过形式化的错误定位算法进行错误定位的程序在二次定位或者多次定位前进行结果预测,引导用户给出高效的反馈信息(期望值或者正确值的确认信息),从而减少错误定位的次数,提高错误定位的效率,实现启发式的错位定位。