基于Logistic回归、ANN、SVM的乳腺癌复发影响因素研究

为找出乳腺癌复发的影响因素,并比较人工神经网络（ANN）型、支持向量机型（SVM）和logistic回归型在乳腺癌复发中的预测效能.本文结合南斯拉夫卢布尔雅那大学医疗中心乳腺癌肿瘤研究所的277例数据,对乳腺癌复发的影响因素进行研究.分别采用了logistic回归、人工神经网络和支持向量机方法来建立乳腺癌复发的预测模型,并对这三种分析方法进行了理论方法和预测效能的比较.结果发现,肿瘤大小、有无结节冒、肿瘤恶性程度（P<0.05）是乳腺癌术后复发的主要影响因素,而在不同的预测方法中相对于logistic回归模型,支持向量机和人工神经网络具有更好的预测效能,其中支持向量机的预测效能最好.     

A deep ensemble network model for classifying and predicting breast cancer

Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta-learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F-score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate.     

Intelligent breast cancer recognition using particle swarm optimization and support vector machines

Breast cancer is the most common cancer among women, except for skin cancer, but early detection of breast cancer improves the chances of survivability. Data mining is widely used for this purpose. As technology develops, large number of breast tumour features are being collected. Using all these features for cancer recognition is expensive and time-consuming. Feature extraction is necessary for increasing the classification accuracy. The goal of this work is to recognise breast cancer using extracted features. To reach this goal, a combination of clustering and classification is used. Particle swarm optimization is used to recognise tumour patterns. The membership degree of each tumour to the patterns is calculated and considered as a new feature. Support vector machine is then employed to classify tumours. Finally this method is analysed in terms of its accuracy, specificity, sensitivity and CPU time consuming using Wisconsin Diagnostic Breast Cancer data set.     

面向乳腺癌辅助诊断的改进支持向量机方法

根据针吸细胞学方法影像中提取的特征值, 设计了一种改进的支持向量机分类方法, 并应用于乳腺癌的辅助诊断。通过对几种常用核函数的对比分析, 所建立的新核函数在诊断中具有很好的综合性能。使用实际临床数据分析显示, 该方法比模因佩雷托(memetic Pareto artificial neural network, MPANN)与一种改进型人工神经网络(evolutionary artificial neural network, EANN)方法在乳腺癌辅助诊断中具有更好的效果, 可以为医疗机构对该疾病的诊断提供有力的决策支持。     

混合和声搜索算法优化SVM的订单优先权评价

订单优先权评价是制订生产计划的关键,针对当前订单优先权评价模型不足,提出一种混合和声搜索算法优化支持向量机的订单优先权评价模型（HHS-SVM）。构建订单优先权评价指标体系,采用支持向量机建立订单优先权评价模型,并采用和声搜索算法优化支持向量机参数,在参数寻优过程中,引入了人工鱼群算法的觅食行为,提高了算法跳出局部最优解的能力和收敛速度,采用仿真实验测试模型的性能。结果表明,相对于对比模型,HHS-SVM提高了订单优先权评价精度,是一种可行、有效的订单优先权评价模型。     

极限学习机在岩性识别中的应用

基于传统支持向量机(SVM)训练速度慢、参数选择难等问题,提出了基于极限学习机(ELM)的岩性识别.该算法是一种新的单隐层前馈神经网络(SLFNs)学习算法,不但可以简化参数选择过程,而且可以提高网络的训练速度.在确定了最优参数的基础上,建立了ELM的岩性分类模型,并且将ELM的分类结果与SVM进行对比.实验结果表明,ELM以较少的神经元个数获得与SVM相当的分类正确率,并且ELM参数选择比SVM简便,有效降低了训练速度,表明了ELM应用于岩性识别的可行性和算法的有效性.     

Support Vector Machine and Artificial Neural Networks for Hydrological Cycles Classifications of a Water Reservoir in the Amazon

This paper presents a methodology based on computational intelligence techniques for classification of hydrological cycles that can infer the change in the physico-chemieal parameters and metals from the water of a reservoir in the Amazon. The methodology initially consists in perform a pre-processing the data to select the most relevant variables of the samples. After that, we compared two different machine learning classifiers, namely SVM （support vector machine） and ANN （artificial neural network）. The automatic model selection is made to choose the parameters of the classifiers. The results indicate that the support vector machine classifier using radial basis function or polynomial kernel exhibited superior results to ANN in terms of overall accuracy and robustness. The SVM classifier accuracy （89.1%） can be considered satisfactory, since there is a great variability of physico-chemical parameters and metals in the hydrological cycles and in the different ecosystems where are the sampling station.     

基于电子鼻技术的花椒品种鉴别方法研究

本文针对不同花椒品种的快速鉴别方法进行研究,以花椒的气味信息检测为研究对象,利用自行研制的电子鼻系统采集了6类花椒样品气味数据,对这些数据样本进行特征提取,得到了56组训练样本和32组测试样本。利用BP神经网络、概率神经网络和支持向量机对特征数据进行鉴别,正确识别率分别为89.58%、93.23%、94.27%,相对于BP神经网络和概率神经网络识别,支持向量机具有更好的分类效果。 本文研制的电子鼻系统能能无损、快速、准确鉴别花椒的品种,为农产品无损检测的研究提供了一种新的思路。     

基于支持向量机的直接逆模型辨识

在简单讨论逆模型辨识原理的基础上,利用支持向量机(SVM)对函数逼近的能力,提出了基于支持向量机的直接逆模型辨识方法.分别采用二次核函数以及高斯RBF核函数,利用训练数据对线性和非线性系统进行黑箱辨识.仿真结果表明,基于支持向量机的直接逆模型辨识方法在处理线性和非线性对象时,辨识性能都优于传统的BP神经网络,不仅辨识精度高,辨识速度快,而且泛化能力较强.     

软件可靠性预测的相关向量机模型

相关向量机是一种解决回归问题非常有效的方法,针对软件失效时间及其之前的m个失效时间数据使用相关向量机进行学习,以建立失效时间之间内在的依赖关系,由此构建新的基于相关向量机的软件可靠性预测模型.在4个数据集上的实验结果表明,新模型在预测能力上较之广泛使用的基于支持向量机或人工神经网络的软件可靠性预测模型有明显的提高,同时也表明现时失效数据的预测能力比很久之前观测的失效数据更强,最后通过实验对合理的m值及不同数据集上核函数参数取值进行研究.     

基于支持向量机特征选择的贝叶斯网结点序

目前较常采用搜索打分方法进行贝叶斯网络结构学习,该方法需要首先依据参与者的经验来确定网络的结点顺序,主观性较强,限制了它的实际应用。基于支持向量机特征选择的方法,可以按照各个结点对叶结点的影响能力进行排序,从而直接从数据中通过学习得出结点顺序,避免了人为因素的影响。实验结果验证了该方法的有效性。     

面向色织生产过程的整经轴数智能预测算法

整经轴数是色织生产中由整经工艺确定的一个品种内待加工的整经轴数目．该参数是制定色织生产作业计划所需的重要特征量。由于整经工艺制定过程非常复杂,其通常需要较长时间才能完成,因而该参数在调度时难以有效获取。为解决复杂色织生产过程调度所需的整经轴数预测问题,提出了一种智能预测算法。该算法针对实际问题特点,将支持向量机与神经网络技术相结合,由支持向量机对与整经轴数相关的参数进行特征提取．并将特征提取后的特征属性作为神经网络的输入,相应的特征属性权重被用以指导神经网络的学习过程。数值计算及实际制造企业应用效果表明该算法是有效的,能满足面向实际色织生产过程的整经轴数预测需要。     

Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis

In this paper, we propose a microcalcification classification scheme, assisted by content-based mammogram retrieval, for breast cancer diagnosis. We recently developed a machine learning approach for mammogram retrieval where the similarity measure between two lesion mammograms was modeled after expert observers. In this work, we investigate how to use retrieved similar cases as references to improve the performance of a numerical classifier. Our rationale is that by adaptively incorporating local proximity information into a classifier, it can help to improve its classification accuracy, thereby leading to an improved “second opinion” to radiologists. Our experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve.     

Combining wavelet-based feature extractions with relevance vector machines for stock index forecasting

Abstract: The relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation has appeared to be a powerful tool for time-series forecasting. The RVM has demonstrated better performance over other methods such as neural networks or autoregressive integrated moving average based models. This study proposes a hybrid model that combines wavelet-based feature extractions with RVM models to forecast stock indices. The time series of explanatory variables are decomposed using some wavelet bases and the extracted time-scale features serve as inputs of an RVM to perform the non-parametric regression and forecasting. Compared with traditional forecasting models, our proposed method performs best. The root-mean-squared forecasting errors are significantly reduced.     

肿瘤诊断中的特征基因提取

基于基因表达谱的特征基因提取方法已经成为当今研究肿瘤分子诊断的热点,但由于基因表达谱数据存在维数过高、样本量很小以及噪音很大等特点,使得肿瘤特征基因选择成为一件有挑战性的工作。提出了一种新的寻找特征基因的方法。首先基于区间间隔或覆盖比的方法来初步选出一些特征基因,而后删掉其中的冗余基因,达到以最少的基因数得到更高的分类准确率的目的。实验采用了3种肿瘤样本集来验证新算法的有效性。针对这3个样本集,只要2或3个特征基因就能得到100％的5-折交叉验证识别准确率。与其他肿瘤分类方法相比,显示了它的优越性。     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

基于机器学习的设备预测性维护方法综述

机器学习算法能够处理高维和多变量数据,并在复杂和动态环境中提取数据中的隐藏关系,在预测性维护技术中具有很好的应用前景。然而,预测性维护系统的性能取决于机器学习算法的选择,对目前应用与预测性维护中的机器学习算法进行综述,详细比较了几种机器学习算法的优缺点,并对机器学习在预测性维护研究中的应用进行了展望。     

支持向量机在人口预测中的应用

支持向量机是一种新型的机器学习方法,该学习方法以结构风险最小化原则取代传统机器学习方法中的经验风险最小化原则,在小样本的机器学习中显示出了优异的性能。将这种新的统计学习方法应用到非线性时间序列预测,并将结果与BP神经网络预测的结果进行比较,结果表明该方法有更高的预测精度。     

花生四烯酸发酵过程的建模方法研究

对发酵法生产花生四烯酸的建模方法进行了初步研究,并基于四种温度下的试验数据,建立了发酵过程模型.比较分析了最小二乘支持向量机( LS-SVM)和广义回归神经网络(GRNN)这两种方法的特点.结果表明,这两种方法均能较好地建立该发酵过程的模型,LS-SVM建模的预测能力稍优于GRNN,为后续花生四烯酸发酵过程的优化及控制的研究奠定了基础.     

人脸特征提取与识别技术研究

人脸识别是模式识别和人工智能领域的一个研究热点,近年来,特征提取与识别作为人脸识别系统的关键环节得到了更为广泛和深入的研究.本文首先简要回顾了人脸识别技术的研究背景及发展历程,对目前人脸特征提取与识别的主要方法进行了分类总结.通过对各种特征提取与识别方法的分析与比较,总结了当前存在的技术困难,并展望了今后的研究趋势.