2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第7页

A Formal Design for the Lexical and Syntax Analyzer of a Pedagogically Effective Subset of C++ 一个教学上有效的c++子集的词法和语法分析器的形式化设计

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0074

M. Farooq, A. Abid, R. Fox

引用次数: 1

Applying the Meta-heuristic Prediction Algorithm for Modeling Power Density in Wind Power Plant 应用元启发式预测算法建模风电场功率密度

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0079

H. Kahraman, M. Ayaz, I. Colak, R. Bayindir

引用次数: 2

Consensus Clustering: A Resampling-Based Method for Building Radiation Hybrid Maps 一致聚类:一种基于重采样的辐射混合地图构建方法

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0047

Raed I. Seetan, J. Bible, Michael Karavias, Wael Seitan, S. Thangiah

{"title":"Consensus Clustering: A Resampling-Based Method for Building Radiation Hybrid Maps","authors":"Raed I. Seetan, J. Bible, Michael Karavias, Wael Seitan, S. Thangiah","doi":"10.1109/ICMLA.2016.0047","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0047","url":null,"abstract":"Building Radiation Hybrid (RH) maps is a challenging process. Traditional RH mapping techniques are very time consuming, and do not work well on noisy datasets. In this presented research, we propose a new approach that uses resampling technique with consensus clustering technique to filter out unreliable markers, and build robust RH maps in a short time. The main aims of using the proposed approach is: first to reduce the mapping computational complexity, thus speeding up the mapping process. And second, to filter out unreliable markers, and map the remaining reliable markers to build robust maps. The proposed approach maps RH datasets in four steps, as follows: 1) uses Jackknife resampling technique to resample the RH dataset, and groups all resampled datasets into clusters. 2) Builds consensus clusters and filters out unreliable markers. 3) Maps the consensus clusters. 4) Connects the consensus clusters' maps to form the final map. To demonstrate the performance of our proposed approach, we compare the accuracy of the constructed maps with the corresponding physical maps. Also, we compare the running time of our constructed maps with the Carthagene tool maps running time. The results show that the proposed approach can construct robust maps in a comparatively very short time.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125328813","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

Deep Learning Anomaly Detection as Support Fraud Investigation in Brazilian Exports and Anti-Money Laundering 深度学习异常检测支持巴西出口欺诈调查和反洗钱

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0172

Ebberth L. Paula, M. Ladeira, Rommel N. Carvalho, Thiago Marzagão

引用次数: 97

Hourly Solar Irradiance Forecasting Based on Machine Learning Models 基于机器学习模型的每小时太阳辐照度预测

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0078

F. Melzi, Taieb Touati, A. Samé, L. Oukhellou

{"title":"Hourly Solar Irradiance Forecasting Based on Machine Learning Models","authors":"F. Melzi, Taieb Touati, A. Samé, L. Oukhellou","doi":"10.1109/ICMLA.2016.0078","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0078","url":null,"abstract":"In recent years, many research studies are conducted into the use of smart meters data for developping decision-making tools including both analytical, forecasting and display purposes. Forecasting energy generation or forecasting energy consumption demand are indeed central problems for urban stakeholders (electricity companies and urban planners). These issues are helpful to allow them ensuring an efficient planning and optimization of energy resources. This paper investigates the problem for forecasting the hourly solar irradiance within a Machine Learning (ML) framework using Similarity method (SIM), Support Vector Machine (SVM) and Neural Network (NN). These approaches rely on a methodology which takes into account the previous hours of the predicting day and also the days having the same number of sunshine hours in the history. The study is conducted on a real data set collected on the Paris suburb of Alfortville. A comparison with two time series approaches namely Naive method and Autoregressive Moving Average Model (ARMA) is performed. This study is the first step towards the development of the hourly solar irradiance forecasting hybrid models.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126125679","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: 24

Automatic Algorithm Selection in Computational Software Using Machine Learning 基于机器学习的计算软件自动算法选择

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0064

M. Simpson, Qing Yi, J. Kalita

引用次数: 3

Bag of Bags: Nested Multi Instance Classification for Prostate Cancer Detection 袋中的袋:前列腺癌检测的嵌套多实例分类

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0032

F. Khalvati, Junjie Zhang, A. Wong, M. Haider

{"title":"Bag of Bags: Nested Multi Instance Classification for Prostate Cancer Detection","authors":"F. Khalvati, Junjie Zhang, A. Wong, M. Haider","doi":"10.1109/ICMLA.2016.0032","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0032","url":null,"abstract":"Computer-aided detection (CAD) algorithms have been proposed for auto-detection of different types of cancer. CAD algorithms rely on machine learning methods to classify regions of interest in images into cancerous and healthy regions. In cancer screening, the foremost problem to solve is whether a patient has cancer, regardless of the location of cancerous regions in the organ. This allows early detection of the disease leading to a right course of action in terms of treatment to be taken. In machine learning, this problem has been formulated as multi-instance learning (MIL) where bags of instances are classified rather than the individual instances. In this paper, we propose a bag of bags (BoB) nested MIL algorithm where high-level bags (or parent bags), each contains multiple smaller bags of instances. We applied the proposed BoB MIL algorithm to prostate cancer detection problem using magnetic resonance imaging data to first detect which patients have cancer and consequently, to detect which slices in the 3D volume imaging data of the detected patients contain cancerous regions. Experimental results obtained from the imaging data of 30 patients with ground-truth data based on biopsy results show that the proposed algorithm is not only capable of detecting prostate cancer at patient level, it is also able to detect the cancerous regions at slice level of imaging data with high accuracy.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124322360","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

Spatial Dependency and Hedonic Housing Regression Model 空间依赖与享乐性住房回归模型

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0097

T. Oladunni, Sharad Sharma

{"title":"Spatial Dependency and Hedonic Housing Regression Model","authors":"T. Oladunni, Sharad Sharma","doi":"10.1109/ICMLA.2016.0097","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0097","url":null,"abstract":"The location of a real estate property has a considerable impact on its appraised value. Accounting for geograph-ical information eliminates some reducible errors in the accuracy of a hedonic housing regression model. An im-proved performance will benefit home buyers, sellers, government and real estate professionals. This paper investigates the spatial dependency and substitutability of submarket and geospatial attributes in a hedonic housing regression model using mutual information (MI) and variance inflation factor (VIF). Best subset linear regression and regression tree predictive models were built as learning algorithms. Bayesian Information Criterion (BIC) and Residual Mean Deviance (RDM) measured the performance of the linear regression and regression trees respectively. The BIC of the linear regression model indicated a best fit at 14 and 11 variables for submarket and geospatial models respectively. Optimization of the submarket tree was attained with 9 parameters comprising of 15 terminal nodes, while 7 parameters comprising of 13 terminal nodes achieved optimization in the geospa-tial tree. While geospatial models have a slight edge over the submarket model, the experiment suggested the substi-tutability of the models. The dataset consisted of single family's homes in 8 counties between January and De-cember 2006 extracted from the Multiple Listing Service repository.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121816234","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

Learning Fairness under Constraints: A Decentralized Resource Allocation Game 约束下的学习公平:一个分散的资源分配博弈

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0043

Qinyun Zhu, J. Oh

引用次数: 4

Relational Synthesis of Text and Numeric Data for Anomaly Detection on Computing System Logs 计算系统日志异常检测的文本与数字数据关联综合

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI: 10.1109/ICMLA.2016.0158

Elisabeth Baseman, S. Blanchard, Zongze Li, Song Fu

{"title":"Relational Synthesis of Text and Numeric Data for Anomaly Detection on Computing System Logs","authors":"Elisabeth Baseman, S. Blanchard, Zongze Li, Song Fu","doi":"10.1109/ICMLA.2016.0158","DOIUrl":"https://doi.org/10.1109/ICMLA.2016.0158","url":null,"abstract":"Monitoring high performance computing systems has become increasingly difficult as researchers and system analysts face the challenge of synthesizing a wide range of monitoring information in order to detect system problems on ever larger machines. We present a method for anomaly detection on syslog data, one of the most important data streams for determining system health. Syslog messages pose a difficult question for analysis because they include a mix of structured natural language text as well as numeric values. We present an anomaly detection framework that combines graph analysis, relational learning, and kernel density estimation to detect unusual syslog messages. We design an event block detector, which finds groups of related syslog messages, to retrieve the entire section of syslog messages associated with a single anomalous line. Our novel approach successfully retrieves anomalous behaviors inserted into syslog files from a virtual machine, including messages indicating serious system problems. We also test our approach on syslog messages from the Trinity supercomputer and find that our methods do not generate significant false positives.","PeriodicalId":356182,"journal":{"name":"2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"383 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131562027","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: 25