2008 Seventh International Conference on Machine Learning and Applications最新文献_第6页

Research on Data Mining Algorithms for Automotive Customers' Behavior Prediction Problem 汽车客户行为预测问题的数据挖掘算法研究

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.23

Lan Huang, Chunguang Zhou, Yu Zhou, Zhe Wang

引用次数: 7

Prediction-Directed Compression of POMDPs 面向预测的pomdp压缩

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.115

Abdeslam Boularias, Masoumeh T. Izadi, B. Chaib-draa

引用次数: 1

Decision Fusion of Machine Learning Models to Predict Radiotherapy-Induced Lung Pneumonitis 机器学习模型的决策融合预测放射治疗引起的肺肺炎

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.122

Shiva K. Das, Shifeng Chen, J. Deasy, Sumin Zhou, F. Yin, L. Marks

{"title":"Decision Fusion of Machine Learning Models to Predict Radiotherapy-Induced Lung Pneumonitis","authors":"Shiva K. Das, Shifeng Chen, J. Deasy, Sumin Zhou, F. Yin, L. Marks","doi":"10.1109/ICMLA.2008.122","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.122","url":null,"abstract":"Combining different machine learning models (decision fusion) has been shown to be an effective method for estimating the underlying physical mechanism by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. To be effective, decision fusion requires that the different models provide some degree of complementary information. In this work, we fuse the results of four different machine learning models (Boosted Decision Trees, Neural Networks, Support Vector Machines, Self Organizing Maps) to predict the risk of lung pneumonitis in patients undergoing thoracic radiotherapy. Fusion was achieved by simple averaging of the 10-fold cross validated predictions for each patient from all four models. To reduce prediction dependence on the manner in which the data set was split, 10-fold cross-validation was repeated 100 times for random data splitting. The area under the receiver operating characteristics curve for the fused cross-validated results was 0.79, higher than the individual models and with (generally) lower variance. The fusion extracted three important features as the consensus among all four models in predicting radiation pneumonitis risk: chemotherapy prior to radiotherapy, equivalent Uniform Dose (EUD) for exponent a = 1.2 to 3, and female gender. The results show great promise for machine learning in radiotherapy outcomes modeling.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116340428","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: 8

Simulation Experiments with Bio-inspired Algorithms for Odor Source Localization in Laminar Wind Flow 层流气流中气味源定位的仿生算法仿真实验

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.128

Thomas Lochmatter, A. Martinoli

引用次数: 29

A Comparison of Three Different Methods for Classification of Breast Cancer Data 三种不同的乳腺癌数据分类方法的比较

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.97

D. Soria, J. Garibaldi, E. Biganzoli, I. Ellis

引用次数: 48

External Validation of a Bayesian Neural Network Model in Survival Analysis 生存分析中贝叶斯神经网络模型的外部验证

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.48

A. Taktak, A. Eleuteri, M. Aung, P. Lisboa, L. Desjardins, B. Damato

引用次数: 4

Handling Large Volumes of Mined Knowledge with a Self-Reconfigurable Topology on Distributed Systems 分布式系统上基于自重构拓扑的海量挖掘知识处理

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.30

Nhien-An Le-Khac, Lamine M. Aouad, Mohand Tahar Kechadi

{"title":"Handling Large Volumes of Mined Knowledge with a Self-Reconfigurable Topology on Distributed Systems","authors":"Nhien-An Le-Khac, Lamine M. Aouad, Mohand Tahar Kechadi","doi":"10.1109/ICMLA.2008.30","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.30","url":null,"abstract":"Nowadays, massive amounts of data which are often geographically distributed and owned by different organisations, are being mined. As consequence, large volumes of knowledge is being generated. This causes the problem of efficient knowledge management in distributed data mining (DDM). The main aim of is to exploit fully the benefit of distributed data analysis while minimising the communication overhead. Existing DDM techniques perform partial analysis of local data at individual sites and then generate global models by aggregating the local results. These two steps are not independent since naive approaches to local analysis may produce incorrect and ambiguous global data models. To overcome this problem, we introduce a distributed knowledge map based on an efficient self-reconfiguration network topology to represent easily and exploit efficiently the knowledge mined in large scale distributed platforms. This will also facilitate the integration/coordination of local mining processes and existing knowledge to build global models. In this paper, we implement this knowledge map and present some preliminary results about its performance.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134196169","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: 3

Multimodal Music Mood Classification Using Audio and Lyrics 使用音频和歌词的多模态音乐情绪分类

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.96

C. Laurier, Jens Grivolla, P. Herrera

引用次数: 227

Speaker Siglet Detection for Business Microscope 商业显微镜扬声器Siglet检测

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.132

J. Nishimura, Nobuo Sato, T. Kuroda

引用次数: 17

Probabilistic Exploitation of the Lucas and Kanade Smoothness Constraint Lucas和Kanade平滑约束的概率利用

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.54

Volker Willert, J. Eggert, Marc Toussaint, E. Körner

引用次数: 4