2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献_第4页

Machine learning of engineering diagnostic knowledge from unstructured verbatim text descriptions 从非结构化逐字文本描述的工程诊断知识的机器学习

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597216

Yinghao Huang, Y. Murphey, Yao Ge

{"title":"Machine learning of engineering diagnostic knowledge from unstructured verbatim text descriptions","authors":"Yinghao Huang, Y. Murphey, Yao Ge","doi":"10.1109/CIDM.2013.6597216","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597216","url":null,"abstract":"This paper presents our research in text mining for discovering important engineering fault diagnostic knowledge from unstructured and verbatim text descriptions. In particular we focus on developing machine learning algorithms for detecting documents that contain descriptions of systematic failures and root causes to the faults. We developed a machine algorithm based on entropy analysis to extract an A-word list, a list of words that are important to characterize the documents of interests, a vector space model to represent features of important documents, and a constraint based k-means clustering algorithm to generate high purity clusters for use in detecting important documents. We applied the algorithms to automotive diagnostic text data, which are unstructured and verbatim descriptions by customers and technicians that contain many typos and self-invented terms. We were able to reduce a list of 2183 words to a list of 137 important words. The classification system generated by these machine learning algorithms showed high recall and accuracy in detecting important diagnostic descriptions.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130076096","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}

引用次数: 2

Detecting abnormal ozone levels using PCA-based GLR hypothesis testing 基于pca的GLR假设检验检测异常臭氧水平

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597223

F. Harrou, M. Nounou, H. Nounou

{"title":"Detecting abnormal ozone levels using PCA-based GLR hypothesis testing","authors":"F. Harrou, M. Nounou, H. Nounou","doi":"10.1109/CIDM.2013.6597223","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597223","url":null,"abstract":"Ozone is one of the lost serious air pollution problems. Monitoring abnormal changes in the concentration of ozone in the troposphere is of great interest because of its negative influence on human health, vegetation, and materials. Modeling ozone is very challenging because of the complexity of the ozone formation mechanisms in the troposphere and the uncertainty about the meteorological conditions in urban areas. In the absence of a process model, principal component analysis (PCA), which is a multivariate statistical technique, has been successfully used as a data-based fault detection (FD) method for highly correlated process variables. When a process model is available, however, the generalized likelihood ratio (GLR) test, which is a statistical hypothesis testing method, has shown good fault detection abilities. In this work, a PCA-based GLR fault detection algorithm is developed to exploit the advantages of the GLR test in the absence of a process model. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. The developed PCA-based GLR FD algorithm is utilized to enhance monitoring the ozone concentrations in Upper Normandy, France. The performances of PCA and PCA-based GLR test are compared through two practical case studies, one involving a sensor fault and the other involving tropospheric ozone pollution in multiple measuring stations. The results show that the PCA-based GLR test can detect abnormal ozone levels with a smaller number of false alarms than the conventional PCA method.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130757968","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}

引用次数: 17

Interpretable models from distributed data via merging of decision trees 通过合并决策树从分布式数据中获得可解释的模型

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597210

A. Andrzejak, Felix Langner, Silvestre Zabala

引用次数: 37

Unsupervised multimodal VAD using sequential hierarchy 使用顺序层次的无监督多模态VAD

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597233

R. Ahmad, Syed Paymaan Raza, Hafiz Malik

引用次数: 6

A hierarchy of independence assumptions for multi-relational Bayes net classifiers 多关系贝叶斯网络分类器的独立假设层次结构

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597230

O. Schulte, B. Bina, B. Crawford, D. Bingham, Y. Xiong

{"title":"A hierarchy of independence assumptions for multi-relational Bayes net classifiers","authors":"O. Schulte, B. Bina, B. Crawford, D. Bingham, Y. Xiong","doi":"10.1109/CIDM.2013.6597230","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597230","url":null,"abstract":"Many databases store data in relational format, with different types of entities and information about their attributes and links between the entities. Link-based classification (LBC) is the problem of predicting the class attribute of a target entity given the attributes of entities linked to it. In this paper we propose a new relational Bayes net classifier method for LBC, which assumes that different links of an object are independently drawn from the same distribution, given attribute information from the linked tables. We show that this assumption allows very fast multi-relational Bayes net learning. We define three more independence assumptions for LBC to unify proposals from different researchers in a single novel hierarchy. Our proposed model is at the top and the wellknown multi-relational Naive Bayes classifier is at the bottom of this hierarchy. The model in each level of the hierarchy uses a new independence assumption in addition to the assumptions used in the higher levels. In experiments on four benchmark datasets, our proposed link independence model has the best predictive accuracy compared to the hierarchy models and a variety of relational classifiers.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128206149","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}

引用次数: 5

How to extract meaningful shapes from noisy time-series subsequences? 如何从噪声时间序列子序列中提取有意义的形状?

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597219

Yanfei Kang, K. Smith‐Miles, D. Belušić

引用次数: 2

Ready-to-use activity recognition for smartphones 智能手机的即时活动识别

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597218

Pekka Siirtola, J. Röning

引用次数: 69

Regularization and improved interpretation of linear data mappings and adaptive distance measures 线性数据映射和自适应距离度量的正则化和改进解释

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597211

M. Strickert, B. Hammer, T. Villmann, Michael Biehl

引用次数: 19

Detecting pixel-value differencing steganography using Levenberg-Marquardt neural network 利用Levenberg-Marquardt神经网络检测像素值差异隐写

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597231

El-Sayed M. El-Alfy

{"title":"Detecting pixel-value differencing steganography using Levenberg-Marquardt neural network","authors":"El-Sayed M. El-Alfy","doi":"10.1109/CIDM.2013.6597231","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597231","url":null,"abstract":"With the wide use of steganographic techniques, several security challenges emerge, e.g. criminals and network intruders can hide any information they want into legitimate multimedia data and exchange it over the Internet. This requires network designers and service providers to investigate new tools for detecting such misuse. In this paper, we explore a detection method based on neural network approach with Levenberg-Marquardt back propagation learning algorithm. This learning technique has been known to overcome the slow convergence of traditional back propagation and the instability problem of the steepest descent optimization method. We focus on digital images containing messages embedded by one of the recently proposed steganographic methods, known as pixel-value differencing. The idea is to analyze images before and after embedding to extract discriminating features and then build a neural network recognition model. The proposed approach is empirically evaluated and compared with four other machine-learning methods. The results show that more than 99% detection rate can be attained with very few false alarms.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114277213","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}

引用次数: 10

Automated selection of interaction effects in sparse kernel methods to predict pregnancy viability 稀疏核方法中相互作用的自动选择预测妊娠存活率

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597213

V. Belle, P. Lisboa

引用次数: 5