2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)最新文献_第5页

Basin Clustering of Turkey by Use of Monthly Stream-Flow Data 基于月流量数据的土耳其流域聚类

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.82

Y. Arslan, Aysenur Birturk, S. Eren

引用次数: 1

Inertia Based Recognition of Daily Activities with ANNs and Spectrotemporal Features 基于神经网络和光谱时间特征的日常活动惯性识别

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.220

Ozsel Kilinc, A. Dalzell, Ismail Uluturk, Ismail Uysal

{"title":"Inertia Based Recognition of Daily Activities with ANNs and Spectrotemporal Features","authors":"Ozsel Kilinc, A. Dalzell, Ismail Uluturk, Ismail Uysal","doi":"10.1109/ICMLA.2015.220","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.220","url":null,"abstract":"As mobile and personal health devices gain in popularity, increasing amounts of data is collected via their embedded sensors such as heart rate monitors and accelerometers. Data analytics and more specifically machine learning algorithms can transform this data into actionable information to improve personal healthcare and quality of life. The main objective of this study is to develop an algorithmic classification framework using feed-forward multilayer perceptrons and statistically rich spectrotemporal features to recognize daily activities based on 3-axis acceleration data. A multitude of MLP topologies and setups, such as different numbers and sizes of hidden layers, supervised output structuring, etc. are tested to comprehensively analyze the clustering capabilities of the artificial neural network for a wide-range of settings. In addition, the contribution of subset of features to classification accuracy is studied to identify respective information potentials and further improve accuracy. Publicly available wrist-worn accelerometer dataset from University of California Irvine's machine learning repository is used for fair comparison with the most recent literature published using the same dataset. Results indicate a significant improvement in recognition rate where the overall accuracy over seven selected activity classes is 91% compared to 54% of the latest publication using the same dataset.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128635473","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

Improved Wind Power Forecasting Using Combination Methods 利用组合方法改进风电预测

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.60

Ceyda Er Koksoy, M. Özkan, S. Buhan, T. Demirci, Y. Arslan, Aysenur Birturk, P. Senkul

{"title":"Improved Wind Power Forecasting Using Combination Methods","authors":"Ceyda Er Koksoy, M. Özkan, S. Buhan, T. Demirci, Y. Arslan, Aysenur Birturk, P. Senkul","doi":"10.1109/ICMLA.2015.60","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.60","url":null,"abstract":"Integration of the wind power into the existing transmission grid is an important issue due to discontinuous and volatile behavior of wind. Moreover, the power plant owners need reliable information about day-ahead power production for market operations. Therefore, wind power forecasting approaches have been gaining importance in renewable energy research area. The Wind Power Monitoring and Forecast System for Turkey (RITM) currently monitors a growing number of wind power plants in Turkey, and uses wind power measurements in addition to different numerical weather predictions to generate short-term power forecasts. Forecasting models of RITM give considerably good results individually. However, forecast combination approaches are frequently used in order not to rely on a single forecast model, and also utilize forecast diversification. In this paper, an analysis of wind power domain and the current wind power forecasting methods of RITM are presented. Then, three main forecast combination approaches, namely Lp-norm based combination, FSS (Fuzzy Soft Sets) based combination and tree-based combination, are proposed to provide better forecasts. These combination methods have been verified on forecasts data of RITM in terms of normalized mean absolute error (NMAE) metric. The experimental results show that all of the applied combination methods give lower NMAE rates for most of the wind power plants compared to individual forecasts.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124957247","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

An Industrial-Strength Pipeline for Recognizing Fasteners 用于识别紧固件的工业强度管道

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.191

Nashlie H. Sephus, Sravan Bhagavatula, Palash Shastri, Eric Gabriel

引用次数: 2

iClass: Combining Multiple Multi-label Classification with Expert Knowledge iClass:多标签分类与专家知识的结合

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.179

Marmar Moussa, Marc Maynard

{"title":"iClass: Combining Multiple Multi-label Classification with Expert Knowledge","authors":"Marmar Moussa, Marc Maynard","doi":"10.1109/ICMLA.2015.179","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.179","url":null,"abstract":"Roper Center is one of the largest public opinion data archives in the world. It collects data sets of polled survey questions from numerous media outlets and organizations. The volume of data introduces search complexities over survey questions and poses challenges when analyzing search trends. Roper Center question-level retrieval applications used human metadata experts to assign topics to content. This has been insufficient to reach required levels of consistency and provides an inadequate base for creating an advanced search experience. The objective of this work is to combine the human expert teams' knowledge of the nature of the survey questions and the concepts and topics these questions express, with the ability of multi-label classifiers to learn this knowledge and apply it to an automated, fast and accurate classification mechanism. This approach cuts down the question analysis and tagging time significantly as well as provides enhanced consistency and scalability for topics' descriptions. At the same time, creating an ensemble of machine learning classifiers combined with expert knowledge is expected to enhance the search experience and provide much needed analytic capabilities to the survey questions databases. In our design, we use classification from several machine learning algorithms like SVM and Decision Trees, combined with expert knowledge in form of handcrafted rules, data analysis and result review. We consolidate the different techniques into a Multipath Classifier with a Confidence point system that decides upon the relevance of topics assigned to survey questions with nearly perfect accuracy.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"51 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121003712","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: 0

An EMD Based Method for Reduction of Ballistocardiogram Artifact from EEG Studies of Evoked Potentials 基于EMD的脑电诱发电位研究中弹道心图伪影还原方法

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.81

Ehtasham Javed, I. Faye, A. Malik, J. Abdullah

引用次数: 1

Robust Vehicle Tracking Using Perceptual Hashing Algorithm 基于感知哈希算法的鲁棒车辆跟踪

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.104

Zheng Li, Jian-Fei Yang, Long Chen, Juan Zha

{"title":"Robust Vehicle Tracking Using Perceptual Hashing Algorithm","authors":"Zheng Li, Jian-Fei Yang, Long Chen, Juan Zha","doi":"10.1109/ICMLA.2015.104","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.104","url":null,"abstract":"Vehicle tracking, significant in the computer vision using machine learning method, allows the vehicle to comprehend its immediate environment and therefore, enhances the intelligence of the vehicles and the safety of vehicle occupants. We propose a novel tracking algorithm that can work robustly under challenging circumstances such as road scene where several kinds of appearance and motion changes of a tracking object occur. Our algorithm is based on the perceptual hashing algorithm (PHA) and the color, low-frequency and rotation information are considered. By means of PHA, our tracker generates a single identification at each frame. The sliding windows produce a series of candidates between consecutive frames so that the new position of tracking object can be updated by comparing the binary code of candidates and identification. In the experiment, the quantitative and qualitative results are expressed by center location error(CLE) and VOC overlap ratio(VOR). Compared to the advanced tracker at present, PHA tracker shows its robustness when confronting violent changes of noise, illumination, background clutter and part occlusion, which demonstrates its state-of-the-art performance in the field of dynamic vehicle tracking.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121181228","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: 0

Intelligent Bus Stop Identification Using Smartphone Sensors 使用智能手机传感器的智能公交车站识别

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.209

K. Srinivasan, K. Kalpakis

{"title":"Intelligent Bus Stop Identification Using Smartphone Sensors","authors":"K. Srinivasan, K. Kalpakis","doi":"10.1109/ICMLA.2015.209","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.209","url":null,"abstract":"Intelligent transportation systems can be built by developing models that learn from the collected transport data. Data collection and implementation of such systems is often costly, and few countries have support for such systems in their transportation budgets. In places where maintaining currency and accuracy of information is difficult, many problems arise. For instance, in Chennai, India, real time bus transit data is not maintained, there is no proper communication about the bus schedules, bus stops are not regularly updated and inconsistent information about bus stops is observed in the transport authority's website. We are interested in developing models for identifying bus stops from trajectories for situations where accurate and current information is not available and traffic conditions are challenging, such as Chennai, India. We develop a simple yet easily accessible Android mobile application (App) to collect GPS traces of bus routes. We use our App to collect GPS trajectory data from Baltimore, Maryland, a place where there are facilities to access up-to-date information about bus stops. We also collect GPS trajectories from Chennai, India. We then develop a model using machine learning techniques to identify bus stops from the collected trajectories. We experimentally evaluate our model by training it on the Baltimore dataset and testing it on the Chennai dataset, achieving testing accuracy between 85 -- 90%. This is comparable to the accuracy of 95% achieved by both training and testing on the Chennai dataset. This illustrates that our approach is effective in helping maintain an accurate and current transport information system for resource constraint environments.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126837036","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

Rejection Factors of Pull Requests Filed by Core Team Developers in Software Projects with High Acceptance Rates 高接受率软件项目中核心团队开发人员提出的拉取请求的拒绝因素

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.41

D. Soares, Manoel Limeira de Lima Júnior, Leonardo Gresta Paulino Murta, A. Plastino

{"title":"Rejection Factors of Pull Requests Filed by Core Team Developers in Software Projects with High Acceptance Rates","authors":"D. Soares, Manoel Limeira de Lima Júnior, Leonardo Gresta Paulino Murta, A. Plastino","doi":"10.1109/ICMLA.2015.41","DOIUrl":"https://doi.org/10.1109/ICMLA.2015.41","url":null,"abstract":"When developers want to contribute to an opensource project, they fork the repository, make changes, and send a pull request to the core team to incorporate these changes back into the repository. However, some projects enforce this collaboration model even for changes made by core team developers. This potentially enhances the quality of the repository by adding an inspection step before accepting a contribution into the repository. In this context, though less frequently, the contributions may be rejected. The understanding of the factors that lead to the rejection of these internal contributions is crucial for the improvement of the ways core developers collaborate, having a direct impact on the team productivity. In this work we extract association rules from pull request data stored in software repositories in order to find factors that have influence over the decision of rejecting contributions made by core developers. In addition, we present a qualitative analysis of some cases, helping to understand the patterns that arose from the association rules. The results indicate that some key factors increase the changes of having internal contributions rejected: (i) the inexperience with pull requests, (ii) the complexity of contributions, as well as the locality of the artifacts that have been modified, and (iii) the contribution policy of the projects.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"755 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126944013","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

Summary Sentence Classification Using Stylometry 用文体学进行摘要句分类

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI: 10.1109/ICMLA.2015.181

R. Shams, Robert E. Mercer

引用次数: 1