朴素贝叶斯算法的改进与应用

朴素贝叶斯算法是分类算法中最经典、最有影响的算法之一,但仍然存在一些不足之处.针对该算法中下溢问题,对算法基本公式进行了优化改进.针对NB算法中准确率问题,结合类别核心词思想和改进后的TFIDF算法,提出了一种基于类别核心词和改进型TFIDF的朴素贝叶斯CIT-NB算法.将改进后的算法应用于新闻数据集文本分类,实验结果表明,CIT-NB算法的分类性能明显优于原始朴素贝叶斯算法和基于TFIDF的分类算法.     

基于主题模型的改进随机森林算法在文本分类中的应用

针对传统随机森林算法在维度高、噪声大的文本分类上出现计算复杂度高和分类效果较差的问题,提出一种基于隐狄利克雷分配(LDA)主题模型的改进随机森林算法。该算法利用LDA主题模型对原始文本建立模型,将原始文本映射到主题空间上,保证了文本主旨与原始文本的一致性,同时也大大降低了文本噪声对分类的影响;并且针对随机森林中决策树特征的随机选择方法,提出在决策树生成过程中,利用对称不确定计算各个特征之间的相关性,从而可以降低不同决策树之间的关联度。最终在主题空间上利用改进的随机森林算法对文本进行分类。经过实验证明,该算法在文本分类上具有良好的优越性。     

基于MapReduce的平均多项朴素贝叶斯文本分类

文本分类技术作为信息检索和文本挖掘的重要基础,近年来得到广泛的关注和快速发展。而互联网上文本数据呈指数级的增长为文本分类带来了新的挑战。针对海量文本分类问题进行了研究,基于分布式计算框架MapReduce平台,实现了一种简单、有效的文本分类算法——平均多项朴素贝叶斯分类方法。实验中该方法分类准确率高于一般朴素贝叶斯方法,且具有较好的加速比。实验结果表明：由于减小了文本冗余特征信息的影响与并行计算良好的扩展性,该方法更适于海量文本数据分类。     

一种双层贝叶斯模型:随机森林朴素贝叶斯

文本分类是自然语言处理领域的一项基础工作.文本数据的高维性和稀疏性,给文本分类带来了许多问题和挑战.朴素贝叶斯模型因其简单、高效、易理解的特点被广泛应用于文本分类任务,但其属性条件独立假设在现实的文本数据中很难满足,从而影响了它的分类性能.为了削弱朴素贝叶斯的属性条件独立假设,学者们提出了许多改进方法,主要包括结构扩展、实例选择、实例加权、特征选择、特征加权等.然而,所有这些方法都是基于独立的单词特征来构建朴素贝叶斯分类模型,在一定程度上限制了它们的分类性能.为此,尝试用特征学习的方法来改进朴素贝叶斯文本分类模型,提出了一种双层贝叶斯模型:随机森林朴素贝叶斯(random forest naive Bayes,RFNB).RFNB分为2层,第1层利用随机森林从原始的单词特征中学习单词组合的高层特征.然后将学习到的新特征输入第2层,经过一位有效编码后用于构建伯努利朴素贝叶斯模型.在大量广泛使用的文本数据集上的实验结果表明,提出的RFNB模型明显优于现有的最先进的朴素贝叶斯文本分类模型和其他经典的文本分类模型.     

基于文本过滤的贝叶斯分类算法的改进

针对传统贝叶斯分类算法无法满足复杂网络文本过滤需求,提出一种多词 贝叶斯分类算法(Multi Word-Bayes,MWB)。该算法一方面引入了特征权重(Term Frequency-Inverse Document Frequency,TF-IDF)的计算思想,优化了传统贝叶斯分类算法只考虑词频不考虑文本间关系的问题;另一方面将词与词间的关系作为文本分类的重要参考项,克服了传统贝叶斯分类算法在分类器训练上对语义分析的忽视。实验结果表明,MWB在垃圾文本过滤上具有更好的分类性能。     

基于加权补集的朴素贝叶斯文本分类算法研究

朴素贝叶斯分类方法由于其简单快速的特点,被广泛应用于文本分类领域。但是当训练集中各个类别的样本数据分布不均匀时,朴素贝叶斯方法分类精度不太理想。针对此问题,提出一种基于加权补集的朴素贝叶斯文本分类算法,该算法利用某个类别的补集的特征来表示当前类别的特征,且对特征权重进行归一化处理。通过实验对比了该方法与传统的朴素贝叶斯方法对文本分类效果的影响,结果表明,基于加权补集的朴素贝叶斯算法具有较好的文本分类效果。     

基于新型不纯度度量的代价敏感随机森林分类器

针对不平衡数据集的有效分类问题,提出一种结合代价敏感学习和随机森林算法的分类器。首先提出了一种新型不纯度度量,该度量不仅考虑了决策树的总代价,还考虑了同一节点对于不同样本的代价差异;其次,执行随机森林算法,对数据集作K次抽样,构建K个基础分类器;然后,基于提出的不纯度度量,通过分类回归树(CART)算法来构建决策树,从而形成决策树森林;最后,随机森林通过投票机制做出数据分类决策。在UCI数据库上进行实验,与传统随机森林和现有的代价敏感随机森林分类器相比,该分类器在分类精度、AUC面积和Kappa系数这3种性能度量上都具有良好的表现。     

基于情感特征向量空间模型的中文商品评论倾向分类算法

为了能够快速有效地将中文商品评论识别为好评或差评,提出一种算法。针对不同类别的商品,预先根据其评论语料构建领域情感词典,评论文本与情感词典集匹配提取情感特征,构建情感特征向量空间模型SF-VSM(Sentiment Feature Vector Space Model),解决传统的特征向量空间模型维数较高及特征选择误差问题。然后基于该模型结合改进的多项式朴素贝叶斯方法对评论进行情感倾向分类。实验结果表明,相比分别基于原始特征和基于χ2特征选取的朴素贝叶斯分类算法,该算法分类精度较高且分类速度快。     

基于随机森林特征重要性的K-匿名特征优选

大数据时代,数据的共享与挖掘存在隐私泄露的安全隐患。针对使用K-匿名隐藏实现隐私保护会大幅降低数据分类挖掘性能问题,提出一种基于随机森林特征重要性的K-匿名特征选择算法(RFKA)用于分类挖掘。使用随机森林特征重要性度量特征的分类性能;采用前向序列搜索策略每次选择不破坏K-匿名且分类性能最大的特征加入特征子集;使用特征子集对应的数据集构建模型进行分类实验。实验结果表明,该算法能更有效地平衡K-匿名和分类挖掘性能,且算法运行效率更高。     

一种基于TF-IDF的朴素贝叶斯算法改进

目前对以朴素贝叶斯算法为代表的文本分类算法,普遍存在特征权重一致,考虑指标单一等问题。为了解决这个问题,提出了一种基于TF-IDF的朴素贝叶斯改进算法TF-IDF-DL朴素贝叶斯算法。该算法以TF-IDF为基础,引入去中心化词频因子和特征词位置因子以加强特征权重的准确性。为了验证该算法的效果,采用了搜狗实验室的搜狗新闻数据集进行实验,实验结果表明,在朴素贝叶斯分类算法中引入TF-IDF-DL算法,能够使该算法在进行文本分类中的准确率、召回率和F 1值都有较好的表现,相比国内同类研究TF-IDF-dist贝叶斯方案,分类准确率提高8.6%,召回率提高11.7%,F 1值提高7.4%。因此该算法能较好地提高分类性能,并且对不易区分的类别也能在一定程度上达到良好的分类效果。     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

View-based model-driven architecture for enhancing maintainability of data access services

In modern service-oriented architectures, database access is done by a special type of services, the so-called data access services (DAS). Though, particularly in data-intensive applications, using and developing DAS are very common today, the link between the DAS and their implementation, e.g. a layer of data access objects (DAOs) encapsulating the database queries, still is not sufficiently elaborated, yet. As a result, as the number of DAS grows, finding the desired DAS for reuse and/or associated documentation can become an impossible task. In this paper we focus on bridging this gap between the DAS and their implementation by presenting a view-based, model-driven data access architecture (VMDA) managing models of the DAS, DAOs and database queries in a queryable manner. Our models support tailored views of different stakeholders and are scalable with all types of DAS implementations. In this paper we show that our view-based and model driven architecture approach can enhance software development productivity and maintainability by improving DAS documentation. Moreover, our VMDA opens a wide range of applications such as evaluating DAS usage for DAS performance optimization. Furthermore, we provide tool support and illustrate the applicability of our VMDA in a large-scale case study. Finally, we quantitatively prove that our approach performs with acceptable response times.     

A pragmatic multi-method investigation of discrepant technological events: Coping,attributions, and ‘accidental’ learning

Discrepant technological events or situations that entail a problem, a misunderstanding or a difficulty with the Information Technology (IT) being employed, are common in the workplace, and can lead to frustration and avoidance behaviors. Little is known, however, about how individuals cope with these events. This paper examines these events by using a multi-method pragmatic approach informed by coping theory. The results of two studies – a critical incident study and an experiment – serve to build and test, respectively, a theoretical model that posits that individuals use a variety of strategies when dealing with these events: they experience negative emotions, make external attributions, and adopt engagement coping strategies directed at solving the event, eventually switching to a disengagement coping strategy when they feel they have no control over the situation. Furthermore, users’ efforts may result in ‘accidental’ learning as they try to overcome the discrepant IT events through engagement coping. The paper ends with a discussion of the results in light of existing literature, future opportunities for research, and implications for practice.     

The Distributed Time-Triggered Simulation Scheme: Core Principles and Supporting Execution Engine

Distributed real-time simulation is a young technology field but its practice is under increasing demands. In recent years the author and his collaborators have been establishing a new approach called the distributed time-triggered simulation (DTS) scheme which is conceptually simple and easy to use but widely applicable. The concept was initiated in the course of developing a new-generation object-oriented real-time programming scheme called the time-triggered message-triggered object (TMO) programming scheme. Some fundamental issues inherent in distributed real-time simulation that were learned during recent experimental studies are discussed along with some approaches for resolving the issues. An execution engine developed to support both the TMOs engaged in control computation and the TMOs engaged in DTS is also discussed along with its possible extensions that will enable significantly larger-scale DTSs.     

Software-Konfigurationsmanagement in großen Softwareprojekten

Zusammenfassung Mit zunehmender Größe der Softwaresysteme verschärfen sich die für die Software-Herstellung typischen Probleme: Beherrschen großer Objektmengen, Erhalten der Systemkonsistenz, Kontrolle der ständigen Änderungseinflüsse und Gewährleisten einer langen Lebensdauer. Die Disziplin Konfigurationsmanagement bildet den methodischen Ansatz, diese Probleme besser zu beherrschen. Software-Konfigurationsmanagement faßt die Herstellung von Softwaresystemen als eine Abfolge von kontrollierten Änderungen an gesicherten Zwischen- und Endergebnissen auf. Dargestellt werden die Objekte und Funktionen des Software-Konfigurationsmanagements sowie die hierfür in großen Software-Projekten benötigten Methoden, Instanzen und Hilfsmittel.     

Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and     

Editorial:Brain-machine interface(BMI) and cyborg intelligence

正Brain-machine interfaces(BMIs)aim at building a direct communication pathway between the brain and an external device,and represent an area of research where significant progress has been made during the past decade.Based on BMIs,mind information can be read out by neural signals to control     

Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and overseas to the world’s scientific circles,especially to stimulate     

SCIENCE CHINA Information Sciences SUBJECT INDEX TO VOLUME 57(2014)

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Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and overseas to the world’s scientific circles,especially to stimulate