2012 11th International Conference on Machine Learning and Applications最新文献_第7页

Unsupervised Disaggregation for Non-intrusive Load Monitoring 非侵入式负荷监测的无监督分解

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.249

S. Pattem

引用次数: 53

Multi-label Collective Classification Using Adaptive Neighborhoods 基于自适应邻域的多标签集体分类

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.77

Tanwistha Saha, H. Rangwala, C. Domeniconi

{"title":"Multi-label Collective Classification Using Adaptive Neighborhoods","authors":"Tanwistha Saha, H. Rangwala, C. Domeniconi","doi":"10.1109/ICMLA.2012.77","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.77","url":null,"abstract":"Multi-label learning in graph-based relational data has gained popularity in recent years due to the increasingly complex structures of real world applications. Collective Classification deals with the simultaneous classification of neighboring instances in relational data, until a convergence criterion is reached. The rationale behind collective classification stems from the fact that an entity in a network (or relational data) is most likely influenced by the neighboring entities, and can be classified accordingly, based on the class assignment of the neighbors. Although extensive work has been done on collective classification of single labeled data, the domain of multi-labeled relational data has not been sufficiently explored. In this paper, we propose a neighborhood ranking method for multi-label classification, which can be further used in the Multi-label Collective Classification framework. We test our methods on real world datasets and also discuss the relevance of our approach for other multi-labeled relational data. Our experimental results show that the use of ranking in neighborhood selection for collective classification improves the performance of the classifier.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126576717","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: 10

A Novel Neural Network Based Control Method with Adaptive On-Line Training for DC-DC Converters 基于神经网络的DC-DC变换器自适应在线训练控制方法

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.152

H. Maruta, M. Motomura, F. Kurokawa

引用次数: 5

Multivariate Assessment of a Repair Program for a New York City Electrical Grid 纽约市电网维修计划的多元评估

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.208

R. Passonneau, Ashish Tomar, Somnath Sarkar, Haimonti Dutta, Axinia Radeva

引用次数: 1

Real-Time Statistical Background Learning for Foreground Detection under Unstable Illuminations 不稳定光照下前景检测的实时统计背景学习

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.85

Dawei Li, Lihong Xu, E. Goodman

{"title":"Real-Time Statistical Background Learning for Foreground Detection under Unstable Illuminations","authors":"Dawei Li, Lihong Xu, E. Goodman","doi":"10.1109/ICMLA.2012.85","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.85","url":null,"abstract":"This work proposes a fast background learning algorithm for foreground detection under changing illumination. Gaussian Mixture Model (GMM) is an effective statistical model in background learning. We first focus on Titterington's online EM algorithm that can be used for real-time unsupervised GMM learning, and then advocate a deterministic data assignment strategy to avoid Bayesian computation. The color of the foreground is apt to be influenced by the environmental illumination that usually produce undesirable effect for GMM updating, however, a collinear feature of pixel intensity under changing light is discovered in RGB color space. This feature is afterward used as a reliable clue to decide which part of mixture to update under changing light. A foreground detection step proposed in early version of this work is employed to extract foreground objects by comparing the estimated background model with the current video frame. Experiments have shown the proposed method is able to achieve satisfactory static background images of scenes as well as is also superior to some mainstream methods in detection performance under both indoor and outdoor scenes.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126715755","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

First Order Statistics Based Feature Selection: A Diverse and Powerful Family of Feature Seleciton Techniques 基于一阶统计量的特征选择:一个多样化和强大的特征选择技术家族

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.192

T. Khoshgoftaar, D. Dittman, Randall Wald, Alireza Fazelpour

{"title":"First Order Statistics Based Feature Selection: A Diverse and Powerful Family of Feature Seleciton Techniques","authors":"T. Khoshgoftaar, D. Dittman, Randall Wald, Alireza Fazelpour","doi":"10.1109/ICMLA.2012.192","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.192","url":null,"abstract":"Dimensionality reduction techniques have become a required step when working with bioinformatics datasets. Techniques such as feature selection have been known to not only improve computation time, but to improve the results of experiments by removing the redundant and irrelevant features or genes from consideration in subsequent analysis. Univariate feature selection techniques in particular are well suited for the large levels of high dimensionality that are inherent in bioinformatics datasets (for example: DNA microarray datasets) due to their intuitive output (a ranked lists of features or genes) and their relatively small computational time compared to other techniques. This paper presents seven univariate feature selection techniques and collects them into a single family entitled First Order Statistics (FOS) based feature selection. These seven all share the trait of using first order statistical measures such as mean and standard deviation, although this is the first work to relate them to one another and consider their performance compared with one another. In order to examine the properties of these seven techniques we performed a series of similarity and classification experiments on eleven DNA microarray datasets. Our results show that in general, each feature selection technique will create diverse feature subsets when compared to the other members of the family. However when we look at classification we find that, with one exception, the techniques will produce good classification results and that the techniques will have similar performances to each other. Our recommendation, is to use the rankers Signal-to-Noise and SAM for the best classification results and to avoid Fold Change Ratio as it is consistently the worst performer of the seven rankers.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130530193","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: 37

A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain 基于脑结构磁共振图像对阿尔茨海默病患者进行三向分类的机器学习管道

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.42

Sriraam Natarajan, Saket Joshi, B. Saha, A. Edwards, Tushar Khot, Elizabeth Moody, K. Kersting, C. Whitlow, J. Maldjian

{"title":"A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain","authors":"Sriraam Natarajan, Saket Joshi, B. Saha, A. Edwards, Tushar Khot, Elizabeth Moody, K. Kersting, C. Whitlow, J. Maldjian","doi":"10.1109/ICMLA.2012.42","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.42","url":null,"abstract":"Magnetic resonance imaging (MRI) has emerged as an important tool to identify intermediate biomarkers of Alzheimer's disease (AD) due to its ability to measure regional changes in the brain that are thought to reflect disease severity and progression. In this paper, we set out a novel pipeline that uses volumetric MRI data collected from different subjects as input and classifies them into one of three classes: AD, mild cognitive impairment (MCI) and cognitively normal (CN). Our pipeline consists of three stages - (1) a segmentation layer where brain MRI data is divided into clinically relevant regions, (2) a classification layer that uses relational learning algorithms to make pair wise predictions between the three classes, and (3) a combination layer that combines the results of the different classes to obtain the final classification. One of the key features of our proposed approach is that it allows for domain expert's knowledge to guide the learning in all the layers. We evaluate our pipeline on 397 patients acquired from the Alzheimer's Disease Neuroimaging Initiative and demonstrate that it obtains state-of the-art performance with minimal feature engineering.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130768548","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: 5

Incremental Mitosis: Discovering Clusters of Arbitrary Shapes and Densities in Dynamic Data 增量有丝分裂:发现动态数据中任意形状和密度的簇

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.26

Rania Ibrahim, N. Ahmed, N. A. Yousri, M. Ismail

引用次数: 4

A Normalized Criterion of Spatial Clustering in Model-Based Framework 基于模型框架的空间聚类归一化准则

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.99

X. Wang, E. Grall-Maës, P. Beauseroy

引用次数: 1

On the Use of SVMs to Detect Anomalies in a Stream of SIP Messages 基于svm的SIP消息流异常检测

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.109

Raihana Ferdous, R. Cigno, A. Zorat

{"title":"On the Use of SVMs to Detect Anomalies in a Stream of SIP Messages","authors":"Raihana Ferdous, R. Cigno, A. Zorat","doi":"10.1109/ICMLA.2012.109","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.109","url":null,"abstract":"Voice and multimedia communications are rapidly migrating from traditional networks to TCP/IP networks (Internet), where services are provisioned by SIP (Session Initiation Protocol). This paper proposes an on-line filter that examines the stream of incoming SIP messages and classifies them as good or bad. The classification is carried out in two stages: first a lexical analysis is performed to weed out those messages that do not belong to the language generated by the grammar defined by the SIP standard. After this first stage, a second filtering occurs which identifies messages that somehow differ - in structure or contents - from messages that were previously classified as good. While the first filter stage is straightforward, as the classification is crisp (either a messages belongs to the language or it does not), the second stage requires a more delicate handling, as it is not a sharp decision whether a message is semantically meaningful or not. The approach we followed for this step is based on using past experience on previously classified messages, i.e. a \"learn-by-example\" approach, which led to a classifier based on Support-Vector-Machines (SVM) to perform the required analysis of each incoming SIP message. The paper describes the overall architecture of the two-stage filter and then explores several points of the configuration-space for the SVM to determine a good configuration setting that will perform well when used to classify a large sample of SIP messages obtained from real traffic collected on a VoIP installation at our institution. Finally, the performance of the classification on additional messages collected from the same source is presented.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130966840","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: 16