2011 10th International Conference on Machine Learning and Applications and Workshops最新文献_第9页

Manifold Learning and Recognition of Human Activity Using Body-Area Sensors 使用身体区域传感器的人体活动的多种学习和识别

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.92

Mi Zhang, A. Sawchuk

引用次数: 26

Relational Classifiers in a Non-relational World: Using Homophily to Create Relations 非关系世界中的关系分类器:使用同态性创建关系

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.122

Sofus A. Macskassy

{"title":"Relational Classifiers in a Non-relational World: Using Homophily to Create Relations","authors":"Sofus A. Macskassy","doi":"10.1109/ICMLA.2011.122","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.122","url":null,"abstract":"Research in the past decade on statistical relational learning (SRL) has shown the power of the underlying network of relations in relational data. Even models built using only relations often perform comparably to models built using sophisticated relational learning methods. However, many data sets -- such as those in the UCI machine learning repository -- contain no relations. In fact, many data sets either do not contain relations or have relations which are not helpful to a specific classification task. The question we investigate in this paper is whether it is possible to construct relations such that relational inference results in better classification performance than non-relational inference. Using simple similarity-based rules to create relations and weighting the strength of these relations using homophily on instance labels, we test whether relational inference techniques are applicable -- in other words, do they perform comparably to standard machine learning algorithms. We show, in an experimental study on 31 UCI benchmark data sets, that relational inference wins more than any of the 6 classifiers we compare against, including a transductive SVM, and that it wins the majority of the time when compared against any one of them.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131753252","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: 6

Extraction of Basic Patterns of Household Energy Consumption 家庭能源消费基本形态的提取

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.68

Haoyang Shen, H. Hino, Noboru Murata, S. Wakao

引用次数: 2

Recognition of Segmented Online Arabic Handwritten Characters of the ADAB Database ADAB数据库中联机阿拉伯语手写字符的分割识别

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.120

S. Abdelazeem, Hany Ahmed

引用次数: 8

Automatic Detection of Excessive Glycemic Variability for Diabetes Management 糖尿病管理中过度血糖变异的自动检测

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.39

Matthew Wiley, Razvan C. Bunescu, C. Marling, J. Shubrook, F. Schwartz

{"title":"Automatic Detection of Excessive Glycemic Variability for Diabetes Management","authors":"Matthew Wiley, Razvan C. Bunescu, C. Marling, J. Shubrook, F. Schwartz","doi":"10.1109/ICMLA.2011.39","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.39","url":null,"abstract":"Glycemic variability, or fluctuation in blood glucose levels, is a significant factor in diabetes management. Excessive glycemic variability contributes to oxidative stress, which has been linked to the development of long-term diabetic complications. An automated screen for excessive glycemic variability, based on the readings from continuous glucose monitoring (CGM) systems, would enable early identification of at risk patients. In this paper, we present an automatic approach for learning variability models that can routinely detect excessive glycemic variability when applied to CGM data. Naive Bayes (NB), Multilayer Perceptron (MP), and Support Vector Machine (SVM) models are trained and evaluated on a dataset of CGM plots that have been manually annotated with respect to glycemic variability by two diabetes experts. In order to alleviate the impact of noise, the CGM plots are smoothed using cubic splines. Automatic feature selection is then performed on a rich set of pattern recognition features. Empirical evaluation shows that the top performing model obtains a state of the art accuracy of 93.8%, substantially outperforming a previous NB model.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131461622","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

Efficient Optimization of Logistic Regression by Direct Use of Conjugate Gradient 直接使用共轭梯度的逻辑回归的有效优化

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.63

Kenji Watanabe, Takumi Kobayashi, N. Otsu

引用次数: 1

Learning with Guaranteed Label Quality 保证标签质量的学习

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.88

Eileen A. Ni, C. Ling

引用次数: 1

Adaptive Time Window Size to Track Concept Drift 自适应时间窗口大小跟踪概念漂移

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.26

M. S. Mouchaweh, J. Zaytoon, P. Billaudel

引用次数: 0

Evaluating Distributional Semantic and Feature Selection for Extracting Relationships from Biological Text 评估生物文本中关系提取的分布语义和特征选择

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.65

Ehsan Emadzadeh, Siddhartha R. Jonnalagadda, Graciela Gonzalez

{"title":"Evaluating Distributional Semantic and Feature Selection for Extracting Relationships from Biological Text","authors":"Ehsan Emadzadeh, Siddhartha R. Jonnalagadda, Graciela Gonzalez","doi":"10.1109/ICMLA.2011.65","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.65","url":null,"abstract":"The constant flow of biomolecular findings being published each day challenges our ability to develop methods to automatically extract the knowledge expressed in text to potentially influence new discoveries. Finding relations between the biological entities (e.g. proteins and genes) in text is a challenging task. To facilitate the extraction process, a relation can be decomposed into a trigger and the complementary arguments (e.g. theme, site). Several approaches have been proposed based on machine learning which generally use a common set of features for all trigger types. Here we evaluate the impact of applying a feature selection method for trigger classification. Our proposed method uses a greedy feature selection algorithm to find an optimal set of attributes for each trigger type. We show that using the customized set of features can improve classification results significantly (up to 53.96% in f-measure). In addition, we evaluated different settings for including semantic features in the classifiers. We found that using semantic features can improve classification results and found the best setting for each trigger type.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"33 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125097275","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

The ROC-Boost Design Algorithm for Asymmetric Classification 非对称分类的ROC-Boost设计算法

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.142

Guido Cesare, R. Manduchi

引用次数: 0