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Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers
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作者 Stephen A.Arhin Adam Gatiba 《Transportation Safety and Environment》 EI 2020年第2期120-132,共13页
The Washington,DC crash statistic report for the period from 2013 to 2015 shows that the city recorded about 41789 crashes at unsignalized intersections,which resulted in 14168 injuries and 51 fatalities.The economic ... The Washington,DC crash statistic report for the period from 2013 to 2015 shows that the city recorded about 41789 crashes at unsignalized intersections,which resulted in 14168 injuries and 51 fatalities.The economic cost of these fatalities has been estimated to be in the millions of dollars.It is therefore necessary to investigate the predictability of the occurrence of theses crashes,based on pertinent factors,in order to provide mitigating measures.This research focused on the development of models to predict the injury severity of crashes using support vector machines(SVMs)and Gaussian naïve Bayes classifiers(GNBCs).The models were developed based on 3307 crashes that occurred from 2008 to 2015.Eight SVM models and a GNBC model were developed.The most accurate model was the SVM with a radial basis kernel function.This model predicted the severity of an injury sustained in a crash with an accuracy of approximately 83.2%.The GNBC produced the worst-performing model with an accuracy of 48.5%.These models will enable transport officials to identify crash-prone unsignalized intersections to provide the necessary countermeasures beforehand. 展开更多
关键词 crashes unsignalized intersection support vector machines Gaussian naïve bayes classifier injury severity
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Roman Urdu News Headline Classification Empowered with Machine Learning 被引量:2
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作者 Rizwan Ali Naqvi Muhammad Adnan Khan +3 位作者 Nauman Malik Shazia Saqib Tahir Alyas Dildar Hussain 《Computers, Materials & Continua》 SCIE EI 2020年第11期1221-1236,共16页
Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak Subcontinent.Persons from the subcontinent may speak the same Urdu language but they might be using different scripts for ... Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak Subcontinent.Persons from the subcontinent may speak the same Urdu language but they might be using different scripts for writing.The communication using the Roman characters,which are used in the script of Urdu language on social media,is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information supply.English Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the past.This is due to the numerous complexities involved in the processing of Roman Urdu data.The complexities associated with Roman Urdu include the non-availability of the tagged corpus,lack of a set of rules,and lack of standardized spellings.A large amount of Roman Urdu news data is available on mainstream news websites and social media websites like Facebook,Twitter but meaningful information can only be extracted if data is in a structured format.We have developed a Roman Urdu news headline classifier,which will help to classify news into relevant categories on which further analysis and modeling can be done.The author of this research aims to develop the Roman Urdu news classifier,which will classify the news into five categories(health,business,technology,sports,international).First,we will develop the news dataset using scraping tools and then after preprocessing,we will compare the results of different machine learning algorithms like Logistic Regression(LR),Multinomial Naïve Bayes(MNB),Long short term memory(LSTM),and Convolutional Neural Network(CNN).After this,we will use a phonetic algorithm to control lexical variation and test news from different websites.The preliminary results suggest that a more accurate classification can be accomplished by monitoring noise inside data and by classifying the news.After applying above mentioned different machine learning algorithms,results have shown that Multinomial Naïve Bayes classifier is giving the best accuracy of 90.17%which is due to the noise lexical variation. 展开更多
关键词 Roman urdu news headline classification long short term memory recurrent neural network logistic regression multinomial naïve bayes random forest k neighbor gradient boosting classifier
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基于单类别学习的自适应数据流分类算法 被引量:1
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作者 张栋 王勇 蔡立军 《西北工业大学学报》 EI CAS CSCD 北大核心 2010年第5期713-717,共5页
目前挖掘概念流动的数据流已经成了研究热点。文章提出了一种既能很好地处理概念漂移又能从单类别中学习的算法UP-AB。通过在超平面数据集和标准数据集上的实验,与PNB[1]算法比较,表明该算法具有更高的准确度,能更快地适应概念漂移。
关键词 数据挖掘 分类器 算法
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基于贝叶斯信念网的网络流量分类与识别研究 被引量:3
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作者 杨彩虹 黄本雄 《计算机应用与软件》 CSCD 2011年第1期216-219,共4页
网络流量分类识别技术是许多网络研究和应用领域的基础,但随着动态端口、端口伪装和信息加密等技术的使用,传统的纯端口识别法已不再有效。提出一种基于贝叶斯信念网的网络流量分类方法,通过使用有向无环图和结点概率表,很好地解决了流... 网络流量分类识别技术是许多网络研究和应用领域的基础,但随着动态端口、端口伪装和信息加密等技术的使用,传统的纯端口识别法已不再有效。提出一种基于贝叶斯信念网的网络流量分类方法,通过使用有向无环图和结点概率表,很好地解决了流属性之间条件独立的问题。对真实网络流量数据的测试结果表明,这种方法具有稳定可靠的分类识别效果。 展开更多
关键词 网络流量分类与识别 机器学习 朴素贝叶斯分类器 贝叶斯信念网
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应用直线集合分割的软件缺陷预测模型 被引量:1
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作者 包祎 王涛 裘国永 《计算机工程与应用》 CSCD 2013年第14期34-38,共5页
缺陷预测能够有效地提升软件测试的效率。基于朴素贝叶斯理论,提出了一个利用平面中点与直线几何关系进行分类的软件缺陷预测模型LGD-NB。LGD-NB有两种工作模式,当其基于最小风险进行决策时,比传统的朴素贝叶斯具有对代价更为精确的描述... 缺陷预测能够有效地提升软件测试的效率。基于朴素贝叶斯理论,提出了一个利用平面中点与直线几何关系进行分类的软件缺陷预测模型LGD-NB。LGD-NB有两种工作模式,当其基于最小风险进行决策时,比传统的朴素贝叶斯具有对代价更为精确的描述;在定义了几何上的高风险决策区域后,LGD-NB可作为元分类器,提供一个可集成其他分类模型进行二次分类的集成框架。实验结果显示:基于最小风险LGD-NB模型的预测性能优于传统的朴素贝叶斯;而集成了SVM算法后的LGD-NB,其预测能力也有较为明显的提升。 展开更多
关键词 软件缺陷预测 朴素贝叶斯 直线集合分割 元分类器 集成框架
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基于减少相似主题分类错误的权重分配新策略
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作者 唐焕玲 王敬东 陆玉昌 《计算机工程与应用》 CSCD 北大核心 2004年第13期185-188,共4页
文本分类的研究者一直在提高文本的分类精度方面做着不懈的努力,在实验中发现,相似主题的文档的分类错误率比较高,该文尝试着提出了一种二次权重分配的新的特征权值分配策略,构造了一种计算难以区分的主题类别的特征辨别能力的权值函数... 文本分类的研究者一直在提高文本的分类精度方面做着不懈的努力,在实验中发现,相似主题的文档的分类错误率比较高,该文尝试着提出了一种二次权重分配的新的特征权值分配策略,构造了一种计算难以区分的主题类别的特征辨别能力的权值函数,目的是减少相似主题类别的文档的分类错误。 展开更多
关键词 向量空间模型(VSM) 特征选择 权值调整 贝叶斯分类
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GIS-based landslide susceptibility modeling:A comparison between fuzzy multi-criteria and machine learning algorithms 被引量:7
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作者 Sk Ajim Ali Farhana Parvin +7 位作者 Jana Vojteková Romulus Costache Nguyen Thi Thuy Linh Quoc Bao Pham Matej Vojtek Ljubomir Gigović Ateeque Ahmad Mohammad Ali Ghorbani 《Geoscience Frontiers》 SCIE CAS CSCD 2021年第2期857-876,共20页
Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.Th... Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Naïve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Naïve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%). 展开更多
关键词 Landslide susceptibility modeling Geographic information system Fuzzy DEMATEL Analytic network process Naïve bayes classifier Random forest classifier
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