2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第4页

Conformal Prediction Using Random Survival Forests 使用随机生存森林的适形预测

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-57

Henrik Boström, L. Asker, R. Gurung, Isak Karlsson, Tony Lindgren, P. Papapetrou

{"title":"Conformal Prediction Using Random Survival Forests","authors":"Henrik Boström, L. Asker, R. Gurung, Isak Karlsson, Tony Lindgren, P. Papapetrou","doi":"10.1109/ICMLA.2017.00-57","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-57","url":null,"abstract":"Random survival forests constitute a robust approach to survival modeling, i.e., predicting the probability that an event will occur before or on a given point in time. Similar to most standard predictive models, no guarantee for the prediction error is provided for this model, which instead typically is empirically evaluated. Conformal prediction is a rather recent framework, which allows the error of a model to be determined by a user specified confidence level, something which is achieved by considering set rather than point predictions. The framework, which has been applied to some of the most popular classification and regression techniques, is here for the first time applied to survival modeling, through random survival forests. An empirical investigation is presented where the technique is evaluated on datasets from two real-world applications; predicting component failure in trucks using operational data and predicting survival and treatment of heart failure patients from administrative healthcare data. The experimental results show that the error levels indeed are very close to the provided confidence levels, as guaranteed by the conformal prediction framework, and that the error for predicting each outcome, i.e., event or no-event, can be controlled separately. The latter may, however, lead to less informative predictions, i.e., larger prediction sets, in case the class distribution is heavily imbalanced.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"96 1","pages":"812-817"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85230241","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: 7

Predicting Hotel Bookings Cancellation with a Machine Learning Classification Model 用机器学习分类模型预测酒店预订取消

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-11

N. António, Ana de Almeida, Luís Nunes

引用次数: 21

Modeling Over-Dispersion for Network Data Clustering 网络数据聚类的过分散建模

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-180

Lu Wang, D. Zhu, Ming Dong, Yan Li

引用次数: 3

MapReduce Based Classification for Fault Detection in Big Data Applications 基于MapReduce分类的大数据故障检测

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-89

M. O. Shafiq, Maryam Fekri, Rami Ibrahim

{"title":"MapReduce Based Classification for Fault Detection in Big Data Applications","authors":"M. O. Shafiq, Maryam Fekri, Rami Ibrahim","doi":"10.1109/ICMLA.2017.00-89","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.00-89","url":null,"abstract":"Recently emerging software applications are large, complex, distributed and data-intensive, i.e., big data applications. That makes the monitoring of such applications a challenging task due to lack of standards and techniques for modeling and analysis of execution data (i.e., logs) produced by such applications. Another challenge imposed by big data applications is that the execution data produced by such applications also has high volume, velocity, variety, and require high veracity, value. In this paper, we present our monitoring solution that performs real-time fault detection in big data applications. Our solution is two-fold. First, we prescribe a standard model for structuring execution logs. Second, we prescribe a Bayesian classification based analysis solution that is MapReduce compliant, distributed, parallel, single pass and incremental. That makes it possible for our proposed solution to be deployed and executed on cloud computing platforms to process logs produced by big data applications. We have carried out complexity, scalability, and usability analysis of our proposed solution that how efficiently and effectively it can perform fault detection in big data applications.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"87 1","pages":"637-642"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85884154","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: 1

Comparing Transfer Learning and Traditional Learning Under Domain Class Imbalance 领域类不平衡下迁移学习与传统学习的比较

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-138

Karl R. Weiss, T. Khoshgoftaar

{"title":"Comparing Transfer Learning and Traditional Learning Under Domain Class Imbalance","authors":"Karl R. Weiss, T. Khoshgoftaar","doi":"10.1109/ICMLA.2017.0-138","DOIUrl":"https://doi.org/10.1109/ICMLA.2017.0-138","url":null,"abstract":"Transfer learning is a subclass of machine learning, which uses training data (source) drawn from a different domain than that of the testing data (target). A transfer learning environment is characterized by the unavailability of labeled data from the target domain, due to data being rare or too expensive to obtain. However, there exists abundant labeled data from a different, but similar domain. These two domains are likely to have different distribution characteristics. Transfer learning algorithms attempt to align the distribution characteristics of the source and target domains to create high-performance classifiers. This paper provides comparative performance analysis between stateof- the-art transfer learning algorithms and traditional machine learning algorithms under the domain class imbalance condition. The domain class imbalance condition is characterized by the source and target domains having different class probabilities, which can create marginal distribution differences between the source and target data. Statistical analysis is provided to show the significance of the results.","PeriodicalId":6636,"journal":{"name":"2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"85 1","pages":"337-343"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76173440","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: 11

Feature Extraction and K-means Clustering Approach to Explore Important Features of Urban Identity 特征提取和k均值聚类方法探索城市身份的重要特征

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00015

Mei-Chih Chang, Peter Bus, G. Schmitt

引用次数: 14

Histogram-Based Asymmetric Relabeling for Learning from Only Positive and Unlabeled Data 基于直方图的非对称重标注学习方法

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.000-8

Tom Arjannikov, G. Tzanetakis

引用次数: 0

Two-phase Parallel Learning to Identify Similar Structures Among Relational Databases 两阶段并行学习识别关系数据库中的相似结构

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-17

Debora G. Reis, Rommel N. Carvalho, Ricardo Silva Carvalho, M. Ladeira

引用次数: 3

A Noise Prediction and Time-Domain Subtraction Approach to Deep Neural Network Based Speech Enhancement 基于深度神经网络的语音增强噪声预测和时域减法

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.0-133

B. O. Odelowo, David V. Anderson

引用次数: 3

Forward Looking Sonar Scene Matching Using Deep Learning 使用深度学习的前视声纳场景匹配

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2017-12-01 DOI: 10.1109/ICMLA.2017.00-99

P. Ribeiro, M. Santos, Paulo L. J. Drews-Jr, S. Botelho

引用次数: 17