Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications最新文献_第4页

A textual representation scheme for identifying clinical relationships in patient records. 用于识别患者记录中临床关系的文本表示方案。

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2011-02-04 DOI: 10.1109/ICMLA.2010.164

Rezarta Islamaj Doğan, Aurélie Névéol, Zhiyong Lu

{"title":"A textual representation scheme for identifying clinical relationships in patient records.","authors":"Rezarta Islamaj Doğan, Aurélie Névéol, Zhiyong Lu","doi":"10.1109/ICMLA.2010.164","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.164","url":null,"abstract":"The identification of relationships between clinical concepts in patient records is a preliminary step for many important applications in medical informatics, ranging from quality of care to hypothesis generation. In this work we describe an approach that facilitates the automatic recognition of relationships defined between two different concepts in text. Unlike the traditional bag-of-words representation, in this work, a relationship is represented with a scheme of five distinct context-blocks based on the position of concepts in the text. This scheme was applied to eight different relationships, between medical problems, treatments and tests, on a set of 349 patient records from the 4th i2b2 challenge. Results show that the context-block representation was very successful (F-Measure = 0.775) compared to the bag-of-words model (F-Measure = 0.402). The advantage of this representation scheme was the correct management of word position information, which may be critical in identifying certain relationships.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"2010 ","pages":"995-998"},"PeriodicalIF":0.0,"publicationDate":"2011-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICMLA.2010.164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30169275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Arabic Handwriting Recognition Using Concavity Features and Classifier Fusion 基于凸性特征和分类器融合的阿拉伯语手写识别

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2011-01-01 DOI: 10.1109/ICMLA.2011.36

S. Abdelazeem, Maha El Meseery

引用次数: 1

Nonlinear RANSAC Optimization for Parameter Estimation with Applications to Phagocyte Transmigration. 非线性RANSAC优化参数估计及其在吞噬细胞迁移中的应用。

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2011-01-01

Mingon Kang, Jean Gao, Liping Tang

{"title":"Nonlinear RANSAC Optimization for Parameter Estimation with Applications to Phagocyte Transmigration.","authors":"Mingon Kang, Jean Gao, Liping Tang","doi":"","DOIUrl":"","url":null,"abstract":"Developing vigorous mathematical equations and estimating accurate parameters within feasible computational time are two indispensable parts to build reliable system models for representing biological properties of the system and for producing reliable simulation. For a complex biological system with limited observations, one of the daunting tasks is the large number of unknown parameters in the mathematical modeling whose values directly determine the performance of computational modeling. To tackle this problem, we have developed a data-driven global optimization method, nonlinear RANSAC, based on RANdom SAmple Consensus (a.k.a. RANSAC) method for parameter estimation of nonlinear system models. Conventional RANSAC method is sound and simple, but it is oriented for linear system models. We not only adopt the strengths of RANSAC, but also extend the method to nonlinear systems with outstanding performance. As a specific application example, we have targeted understanding phagocyte transmigration which is involved in the fibrosis process for biomedical device implantation. With well-defined mathematical nonlinear equations of the system, nonlinear RANSAC is performed for the parameter estimation. In order to evaluate the general performance of the method, we also applied the method to signalling pathways with ordinary differential equations as a general format.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"1 ","pages":"501-504"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3516195/pdf/nihms336836.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31113556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Classification Approach for Risk Prognosis of Patients on Mechanical Ventricular Assistance. 机械心室辅助患者风险预后的分类方法。

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.50

Yajuan Wang, Carolyn Penstein Rosé, Antonio Ferreira, Dennis M McNamara, Robert L Kormos, James F Antaki

引用次数: 13

The Upper and Lower Bounds of the Prediction Accuracies of Ensemble Methods for Binary Classification. 二元分类集成方法预测精度的上界和下界。

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.62

Xueyi Wang, Nicholas J Davidson

引用次数: 9

Non-Alignment Features Based Enzyme/Non-Enzyme Classification Using an Ensemble Method. 基于非对准特征的酶/非酶集成分类。

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.167

Nicholas J Davidson, Xueyi Wang

{"title":"Non-Alignment Features Based Enzyme/Non-Enzyme Classification Using an Ensemble Method.","authors":"Nicholas J Davidson, Xueyi Wang","doi":"10.1109/ICMLA.2010.167","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.167","url":null,"abstract":"As a growing number of protein structures are resolved without known functions, using computational methods to help predict protein functions from the structures becomes more and more important. Some computational methods predict protein functions by aligning to homologous proteins with known functions, but they fail to work if such homology cannot be identified. In this paper we classify enzymes/non-enzymes using non-alignment features. We propose a new ensemble method that includes three support vector machines (SVM) and two k-nearest neighbor algorithms (k-NN) and uses a simple majority voting rule. The test on a data set of 697 enzymes and 480 non-enzymes adapted from Dobson and Doig shows 85.59% accuracy in a 10-fold cross validation and 86.49% accuracy in a leave-one-out validation. The prediction accuracy is much better than other non-alignment features based methods and even slightly better than alignment features based methods. To our knowledge, our method is the first time to use ensemble methods to classify enzymes/non-enzymes and is superior over a single classifier.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":" ","pages":"546-551"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICMLA.2010.167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29883011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Empowering Simultaneous Feature and Instance Selection in Classification Problems through the Adaptation of Two Selection Algorithms 通过两种选择算法的适配，增强分类问题中特征和实例的同时选择能力

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.121

R. D. Carmo, F. Freitas, J. Souza

引用次数: 2

Boundary Constrained Manifold Unfolding 边界约束流形展开

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2008-01-01 DOI: 10.1109/ICMLA.2008.65

Bo Liu, Hongbin Zhang, Wenan Chen

引用次数: 1

Supervised Reinforcement Learning Using Behavior Models 使用行为模型的监督强化学习

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.102

Victor Uc-Cetina

引用次数: 4

Improving gene expression programming performance by using differential evolution 利用差分进化改进基因表达编程性能

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.55

Qiongyun Zhang, Chi Zhou, Weimin Xiao, Peter C. Nelson

引用次数: 10