At present, with rapid development of China's urbanization, the population density increases, the structure of buildings become more complexity, and building materials and techniques emerge endlessly. Frequent unsafe personal behavior and complex external unsafe factors bring more uncontrollable influences on preventing and controlling fire hazard of buildings in urban area. Traditional methods of fire hazard analysis have limitations on fire hazards forecasting in complex urban areas. This paper presents a data mining method based on Bayesian Network for fire hazard analysis of urban buildings. Based on the historical records of fire incidents in a city of China in past three years, from 2014 to 2016, we analyze the potential fire risk according to building properties and outside influences of buildings. We process and analyze the data, and construct a Bayesian Network based on the analytic results and the actual fire extinguishing situation. After that, we train the model with positive samples and negative samples. At last, we use the Bayesian Network model to assess the risks of building fire hazards. By using ROC curve to analyze the accuracy of the model, we get accurate and stable results. Based on Bayesian Network model with building property and external influence, the building fire risk probability is about 1.0×10-9 to 1.0×10-12. We also introduce another machine learning method, Logistic Regression algorithm to evaluate the performance of Bayesian Network model. The results show that our Bayesian Network model can achieve better performance.
{"title":"A Data Mining Method for Potential Fire Hazard Analysis of Urban Buildings based on Bayesian Network","authors":"Xin Liu, Yutong Lu, Zijun Xia, Feifei Li, Tianqi Zhang","doi":"10.1145/3144789.3144811","DOIUrl":"https://doi.org/10.1145/3144789.3144811","url":null,"abstract":"At present, with rapid development of China's urbanization, the population density increases, the structure of buildings become more complexity, and building materials and techniques emerge endlessly. Frequent unsafe personal behavior and complex external unsafe factors bring more uncontrollable influences on preventing and controlling fire hazard of buildings in urban area. Traditional methods of fire hazard analysis have limitations on fire hazards forecasting in complex urban areas. This paper presents a data mining method based on Bayesian Network for fire hazard analysis of urban buildings. Based on the historical records of fire incidents in a city of China in past three years, from 2014 to 2016, we analyze the potential fire risk according to building properties and outside influences of buildings. We process and analyze the data, and construct a Bayesian Network based on the analytic results and the actual fire extinguishing situation. After that, we train the model with positive samples and negative samples. At last, we use the Bayesian Network model to assess the risks of building fire hazards. By using ROC curve to analyze the accuracy of the model, we get accurate and stable results. Based on Bayesian Network model with building property and external influence, the building fire risk probability is about 1.0×10-9 to 1.0×10-12. We also introduce another machine learning method, Logistic Regression algorithm to evaluate the performance of Bayesian Network model. The results show that our Bayesian Network model can achieve better performance.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124435258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Narongrid Tangpathompong, U. Suksawatchon, J. Suksawatchon
Data stream clustering is becoming the efficient method to cluster an online massive data. The clustering task requires a process capable of partitioning data continuously with incremental learning method. In this paper, we present a new clustering method, called DyHEMstream, which is online and offline algorithm. In online phase, dynamic hyper-ellipsoidal micro-cluster is proposed used to keep summary information about evolving data stream based on new incoming data sample. The shape of proposed micro-cluster can represent the incoming data better than traditional micro-cluster. The algorithm processes each data point in one-pass fashion without storing the entire data set. In offline phase, each cluster is generated by expanding hyper-ellipsoidal micro-clusters to form the final clusters. The DyHEMstream algorithm is evaluated on various synthetic data sets using different quality metrics compared with a famous data stream clustering -- DenStream. Based on purity, Rand index, and Jaccard index, DyHEMstrem is very efficient than DenStream in term of clustering quality in different shapes, sizes, and densities in noisy data.
{"title":"The Dynamic Hyper-ellipsoidal Micro-Clustering for Evolving Data Stream Using Only Incoming Datum","authors":"Narongrid Tangpathompong, U. Suksawatchon, J. Suksawatchon","doi":"10.1145/3144789.3144818","DOIUrl":"https://doi.org/10.1145/3144789.3144818","url":null,"abstract":"Data stream clustering is becoming the efficient method to cluster an online massive data. The clustering task requires a process capable of partitioning data continuously with incremental learning method. In this paper, we present a new clustering method, called DyHEMstream, which is online and offline algorithm. In online phase, dynamic hyper-ellipsoidal micro-cluster is proposed used to keep summary information about evolving data stream based on new incoming data sample. The shape of proposed micro-cluster can represent the incoming data better than traditional micro-cluster. The algorithm processes each data point in one-pass fashion without storing the entire data set. In offline phase, each cluster is generated by expanding hyper-ellipsoidal micro-clusters to form the final clusters. The DyHEMstream algorithm is evaluated on various synthetic data sets using different quality metrics compared with a famous data stream clustering -- DenStream. Based on purity, Rand index, and Jaccard index, DyHEMstrem is very efficient than DenStream in term of clustering quality in different shapes, sizes, and densities in noisy data.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129068738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A new method based on the total variation model, applicable to reconstruct the compressed sensing color images, is proposed. At first, the compressed sensing color images should be converted form the RGB color space to the CMYK space, and the compressed sensing color images in the CMYK space can match exactly with the quaternion matrix. Next, the amplitude and the different four phase information of the quaternion matrix is treated as the smoothing constraints for the compressed sensing problem in order to reconstruct the color images more effectively. Finally, the gradient projection method is used to solve the compressed sensing problem. Experimental results show that this new method can reconstruct color images better than some traditional methods.
{"title":"A New Method for Compressed Sensing Color Images Reconstruction Based on Total Variation Model","authors":"Fan Liao, Shuai Shao","doi":"10.1145/3144789.3144810","DOIUrl":"https://doi.org/10.1145/3144789.3144810","url":null,"abstract":"A new method based on the total variation model, applicable to reconstruct the compressed sensing color images, is proposed. At first, the compressed sensing color images should be converted form the RGB color space to the CMYK space, and the compressed sensing color images in the CMYK space can match exactly with the quaternion matrix. Next, the amplitude and the different four phase information of the quaternion matrix is treated as the smoothing constraints for the compressed sensing problem in order to reconstruct the color images more effectively. Finally, the gradient projection method is used to solve the compressed sensing problem. Experimental results show that this new method can reconstruct color images better than some traditional methods.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"1150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127431649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multi-label classification with many labels are common in real-world application. However, traditional multi-label classifiers often become computationally inefficient for hundreds or even thousands of labels. Therefore, the label space dimension reduction is designed to address this problem. In this paper, the existing studies of label space dimension reduction are summarized; especially, these studies were classified into two categories: label space dimension reduction based on transformed labels and label subset; meanwhile, we analyze the studies belonging to each type and give the experimental comparison of two typical LSDR algorithms. To the best of our knowledge, this is the first effort to review the development of label space dimension reduction.
{"title":"An Overview of Label Space Dimension Reduction for Multi-Label Classification","authors":"L. Tang, Lin Liu, Jianhou Gan","doi":"10.1145/3144789.3144807","DOIUrl":"https://doi.org/10.1145/3144789.3144807","url":null,"abstract":"Multi-label classification with many labels are common in real-world application. However, traditional multi-label classifiers often become computationally inefficient for hundreds or even thousands of labels. Therefore, the label space dimension reduction is designed to address this problem. In this paper, the existing studies of label space dimension reduction are summarized; especially, these studies were classified into two categories: label space dimension reduction based on transformed labels and label subset; meanwhile, we analyze the studies belonging to each type and give the experimental comparison of two typical LSDR algorithms. To the best of our knowledge, this is the first effort to review the development of label space dimension reduction.","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132941830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 2nd International Conference on Intelligent Information Processing","authors":"","doi":"10.1145/3144789","DOIUrl":"https://doi.org/10.1145/3144789","url":null,"abstract":"","PeriodicalId":254163,"journal":{"name":"Proceedings of the 2nd International Conference on Intelligent Information Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125804502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：