2008 Seventh International Conference on Machine Learning and Applications最新文献_第4页

Nonlinear Kernel-Based Approaches for Predicting Normal Tissue Toxicities 基于非线性核的正常组织毒性预测方法

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.126

I. El-Naqa, J. Bradley, J. Deasy

{"title":"Nonlinear Kernel-Based Approaches for Predicting Normal Tissue Toxicities","authors":"I. El-Naqa, J. Bradley, J. Deasy","doi":"10.1109/ICMLA.2008.126","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.126","url":null,"abstract":"Since the early demonstration of the curative potential of radiation therapy for tumor sterilization, normal tissue toxicity continues to be dose limiting. Accurate prediction of patient¿s complication risk would allow personalization of treatment planning decisions. Nonlinear kernel methods can provide a robust framework for learning complex interactions between observed toxicities and treatment, anatomical, and patient-related variables. However, proper application of these powerful methods would require better understanding of a high-dimensional feature space that is spanned by all these variables. In this work, we investigate methods for visualization of this high-dimensional space and compare different approaches for extracting discriminant features. Our preliminary results demonstrate that principle component analysis is a valuable tool for visualizing high dimensional data and for determining proper kernel type. In addition, variable selection based on resampling methods within the logistic regression framework seemed to yield improved prediction performance compared to the recursive-feature elimination method.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"57 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":"121982833","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

Microarray Classification from Several Two-Gene Expression Comparisons 几个双基因表达比较的微阵列分类

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.152

D. Geman, Bahman Afsari, A. Tan, D. Naiman

引用次数: 15

Segmentation of Lesions with Improved Specificity in Computer-Aided Diagnosis Using a Massive-Training Artificial Neural Network (MTANN) 大规模训练人工神经网络(MTANN)在计算机辅助诊断中提高病灶特异性的分割

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.112

Kenji Suzuki

{"title":"Segmentation of Lesions with Improved Specificity in Computer-Aided Diagnosis Using a Massive-Training Artificial Neural Network (MTANN)","authors":"Kenji Suzuki","doi":"10.1109/ICMLA.2008.112","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.112","url":null,"abstract":"Segmentation of lesions plays an important role in computer-aided diagnostic (CAD) schemes, because the accuracy of segmentation affects the accuracy of the feature extraction and analysis based on segmented lesions, and therefore, the final accuracy of classification. Accurate segmentation is difficult especially for complicated patterns such as lesions overlapping or touching normal structures, low-contrast lesions, and subtle opacities. With standard segmentation methods, normal structures overlapping or touching lesions are often erroneously included in segmented regions. In addition, normal structures are often segmented erroneously as lesions. Thus, improving the specificity of segmentation methods is very important in the development of a CAD scheme. Our purpose in this study was to develop a supervised lesion segmentation method based on a massive-training artificial neural network (MTANN) filter in a CAD scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to segment nodules with improved specificity. With the MTANN-based segmentation method, the specificity of the segmentation was improved; thus, the overall performance of our CAD scheme was improved substantially.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"1 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":"128839203","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

Improving Accuracy in the Montgomery County Corrections Program Using Case-Based Reasoning 运用基于案例的推理提高蒙哥马利县矫正项目的准确性

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.94

C. Soares, Christina S. Hamilton, L. Montgomery, J. Gilbert

引用次数: 1

Highly Scalable SVM Modeling with Random Granulation for Spam Sender Detection 基于随机粒化的高可扩展SVM模型用于垃圾邮件发送者检测

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.51

Yuchun Tang, Yuanchen He, S. Krasser

引用次数: 8

Automated Microarray Classification Based on P-SVM Gene Selection 基于P-SVM基因选择的微阵列自动分类

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.75

Johannes Mohr, Sambu Seo, K. Obermayer

引用次数: 3

Prioritizing Health Promotion Plans with k-Bayesian Network Classifier 基于k-贝叶斯网络分类器的健康促进计划优先排序

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.117

Ken Ueno, Toshio Hayashi, K. Iwata, Nobuyoshi Honda, Youichi Kitahara, T. Paul

{"title":"Prioritizing Health Promotion Plans with k-Bayesian Network Classifier","authors":"Ken Ueno, Toshio Hayashi, K. Iwata, Nobuyoshi Honda, Youichi Kitahara, T. Paul","doi":"10.1109/ICMLA.2008.117","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.117","url":null,"abstract":"Recently, Bayesian network classifiers (BNCs) have attracted many researchers because they can produce classification models with dependencies among attributes. From the application viewpoint, however, BNCs sometimes produce models too complicated to interpret easily. In this paper, we propose k-Bayesian network classifier (k-BNC), which is a new method to reconstruct the attribute-dependency relationship from data for health promotion planning. From the health promotion viewpoint, it would be highly advantageous if occupational physicians could make effective plans for employees, and if employees could carry out the plans easily. Therefore, we focus on the attribute dependencies in classification models represented as a directed acyclic graph (DAG), and find the effective attributes by measuring the standardized Kullback-Leibler divergence from parent attributes to their children. In experimental evaluation, we firstly compare the accuracy of k-BNC with that of Naive Bayes Classifiers, and other wellknown Bayesian Networks and structure learning methods (k2 algorithm etc.) on some public datasets. We show that our proposed k-BNC method successfully produces classification models for the prioritization of health promotion plans on our health checkup data.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"39 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113933946","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: 4

Development of CPT_M3D for Multiple Chemical Plume Tracing and Source Identification CPT_M3D多化学品羽流追踪与源识别技术的开发

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.64

Joseph E. Sutton, Wei Li

引用次数: 19

A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation 无监督时间序列分割中切换线性高斯状态空间模型的贝叶斯方法

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.109

S. Chiappa

引用次数: 7

Efficient Hiding of Collaborative Recommendation Association Rules with Updates 带更新的协同推荐关联规则的有效隐藏

2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.33

Shyue-Liang Wang, Ting-Zheng Lai, T. Hong, Yu-Lung Wu

引用次数: 0