2013 12th International Conference on Machine Learning and Applications最新文献

筛选
英文 中文
Computed Data-Geometry Based Supervised and Semi-supervised Learning in High Dimensional Data 基于计算数据几何的高维数据有监督和半监督学习
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.56
Elizabeth P. Chou, F. Hsieh, J. Capitanio
{"title":"Computed Data-Geometry Based Supervised and Semi-supervised Learning in High Dimensional Data","authors":"Elizabeth P. Chou, F. Hsieh, J. Capitanio","doi":"10.1109/ICMLA.2013.56","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.56","url":null,"abstract":"In most high dimensional settings, constructing supervised or semi-supervised learning rules has been facing various critically difficult issues, such as no visualizing tools for empirical guidance, no valid distance measures, and no suitable variable selection methods for proper discrimination among data nodes. We attempt to alleviate all of these difficulties by computing data-geometry via a recently developed computational algorithm called Data Cloud geometry (DCG). The computed geometry is represented by a hierarchy of clusters providing a base for developing a divide-and-conquer version of a learning approach. We demonstrate the advantages of taking posteriori geometric information into learning rules construction by evaluating its performance with many illustrated examples and several real data sets compared to the performance resulting from the majority of commonly used techniques.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133942155","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
Online Processing of Social Media Data for Emergency Management 面向应急管理的社交媒体数据在线处理
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.83
Daniela Pohl, A. Bouchachia, H. Hellwagner
{"title":"Online Processing of Social Media Data for Emergency Management","authors":"Daniela Pohl, A. Bouchachia, H. Hellwagner","doi":"10.1109/ICMLA.2013.83","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.83","url":null,"abstract":"Social media offers an opportunity for emergency management to identify issues that need immediate reaction. To support the effective use of social media, an analysis approach is needed to identify crisis-related hotspots. We consider in this investigation the analysis of social media (i.e., Twitter, Flickr and YouTube) to support emergency management by identifying sub-events. Sub-events are significant hotspots that are of importance for emergency management tasks. Aiming at sub-event detection, recognition and tracking, the data is processed online in real-time. We introduce an incremental feature selection mechanism to identify meaningful terms and use an online clustering algorithm to uncover sub-events on-the-fly. Initial experiments are based on tweets enriched with Flickr and YouTube data collected during Hurricane Sandy. They show the potential of the proposed approach to monitor sub-events for real-world emergency situations.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133970605","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: 9
Fuzzy Model Tree for Early Effort Estimation 早期工作量估算的模糊模型树
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.115
Mohammad Azzeh, A. B. Nassif
{"title":"Fuzzy Model Tree for Early Effort Estimation","authors":"Mohammad Azzeh, A. B. Nassif","doi":"10.1109/ICMLA.2013.115","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.115","url":null,"abstract":"Use Case Points (UCP) is a well-known method to estimate the project size, based on Use Case diagram, at early phases of software development. Although the Use Case diagram is widely accepted as a de-facto model for analyzing object oriented software requirements over the world, UCP method did not take sufficient amount of attention because, as yet, there is no consensus on how to produce software effort from UCP. This paper aims to study the potential of using Fuzzy Model Tree to derive effort estimates based on UCP size measure using a dataset collected for that purpose. The proposed approach has been validated against Tree boost model, Multiple Linear Regression and classical effort estimation based on the UCP model. The obtained results are promising and show better performance than those obtained by classical UCP, Multiple Linear Regression and slightly better than those obtained by Tree boost model.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125195506","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: 19
The Estimation of Students' Academic Success by Data Mining Methods 用数据挖掘方法评估学生学业成绩
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.173
Hanife Goker, H. Bulbul, E. Irmak
{"title":"The Estimation of Students' Academic Success by Data Mining Methods","authors":"Hanife Goker, H. Bulbul, E. Irmak","doi":"10.1109/ICMLA.2013.173","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.173","url":null,"abstract":"Data mining is a process of getting out useful information from data stacks. One of the most common application areas is to use classification of algorithms that estimate the future events by past experiences. In this context, in order to predict future events, a data warehouse is created by using the background of students which includes demographic, personal, school, and course information of students. On this data warehouse by using classification algorithms, new applications which can make inferences for the future could be developed. Aims of this study are to create student data warehouse which can be used data mining algorithms, to improve an early warning system that may estimate students' the future academic successes for students and also for their families and to find out primary factors affecting their academic success.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133827980","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
Approach to Cold-Start Problem in Recommender Systems in the Context of Web-Based Education 网络教育背景下推荐系统冷启动问题的研究
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.199
R. Gotardo, Estevam Hruschka, S. Zorzo
{"title":"Approach to Cold-Start Problem in Recommender Systems in the Context of Web-Based Education","authors":"R. Gotardo, Estevam Hruschka, S. Zorzo","doi":"10.1109/ICMLA.2013.199","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.199","url":null,"abstract":"In this paper we present an approach to treatment of the Cold-Start Problem in Recommendation System for Environment Education Web. Our approach is based on the concept of Coupled-Learning and Bootstrapping. Based on an initial set of data we apply algorithms traditional machine learning to cooperate with each other, forming various views on its outputs and allowing the data set to be classified incrementally. Thus, it is possible to increase the initial volume of data and to improve the performance of a recommender more instances for analysis. The vast majority of the efforts attack the cold start problem with variations of the CBF algorithm. In our approach, we use the incremental semi-supervised learning based on pairs in order to increase the initial training set in order to allow the generation of more recommendations.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"343 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124238951","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
Epidemiological Data Analysis in TerraFly Geo-spatial Cloud TerraFly地理空间云中的流行病学数据分析
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.166
Huibo Wang, Yun Lu, Yudong Guang, Erik Edrosa, Mingjin Zhang, Raul Camarca, Y. Yesha, T. Lucic, N. Rishe
{"title":"Epidemiological Data Analysis in TerraFly Geo-spatial Cloud","authors":"Huibo Wang, Yun Lu, Yudong Guang, Erik Edrosa, Mingjin Zhang, Raul Camarca, Y. Yesha, T. Lucic, N. Rishe","doi":"10.1109/ICMLA.2013.166","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.166","url":null,"abstract":"GIS systems and online services are growing at a very fast pace, however, there are few online services for the analysis of geospatial epidemiology and their functionality is limited. We present a geospatial epidemiology analysis system on the TerraFly Geo-spatial Cloud platform. The system provides comprehensive spatial analysis methods and visualization. In this system, the user is not required to program in order to employ the functionality. All the datasets are stored in the Geo-spatial Cloud. This system is accessible at http://terrafly.fiu.edu/GeoCloud/. The system API algorithms adapted to geospatial epidemiology. The application utilizes the GeoCloud distributed storage system for the Big Data to be analyzed, it utilizes an interactive mapping API to display results.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121924826","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: 2
Scalable and Locally Applicable Measures of Treatment Variation That Use Hospital Billing Data 使用医院计费数据的可扩展和本地适用的治疗变化措施
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.159
Michael A. Vedomske, M. Gerber, Donald E. Brown, J. Harrison
{"title":"Scalable and Locally Applicable Measures of Treatment Variation That Use Hospital Billing Data","authors":"Michael A. Vedomske, M. Gerber, Donald E. Brown, J. Harrison","doi":"10.1109/ICMLA.2013.159","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.159","url":null,"abstract":"Care variation studies often use large amounts of data but approaches developed for such research are either scalable but not locally applicable or locally applicable but not scalable. We present a method that is scalable and locally applicable while being statistically significant. Using a population of patients diagnosed with both congestive heart failure and myocardial infarction, we developed and tested measures of care variation on data derived from hospital billing records. Our metrics yielded statistically significant results. Computing time for the method was found to increase linearly allowing for the desired scalability. In the future, our care variation metrics be used to gain insight into local conditions that correlate with outcomes of interest like visit charges or morbidity rates.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124427476","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
Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem 作业车间调度问题的改进助手-目标优化策略
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.151
Irina Petrova, Arina Buzdalova, M. Buzdalov
{"title":"Improved Helper-Objective Optimization Strategy for Job-Shop Scheduling Problem","authors":"Irina Petrova, Arina Buzdalova, M. Buzdalov","doi":"10.1109/ICMLA.2013.151","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.151","url":null,"abstract":"A single-objective optimization problem can be solved more efficiently by introducing some helper-objectives and running a multi-objective evolutionary algorithm. But what objectives should be used at each optimization stage? This paper describes a new method of adaptive helper-objectives selection in multi-objective evolutionary algorithms. The proposed method is applied to the Job-Shop scheduling problem and compared with the previously known approach, which was specially developed for the Job-Shop problem. A comparison with the previously proposed method of adaptive helper-objective selection based on reinforcement learning is performed as well.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125780226","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
Hybrid Method for Fast SVM Training in Applications Involving Large Volumes of Data 在大数据量应用中快速训练SVM的混合方法
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.195
M. Wani
{"title":"Hybrid Method for Fast SVM Training in Applications Involving Large Volumes of Data","authors":"M. Wani","doi":"10.1109/ICMLA.2013.195","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.195","url":null,"abstract":"One of the problems of training a Support Vector Machine (SVM) for applications involving large volumes of data is how to solve the constrained quadratic programming issue. The optimization process suffers from the problem of large memory requirement and computation time. In this paper we propose a hybrid genetic algorithm based SVM that addresses the large memory requirement and computation time problem. The system operates in two main stages. During first stage it obtains a subset of features using genetic algorithm and during second stage it uses genetic algorithm to train the SVM using subset of features. The proposed system is tested on gene expression profile data sets. The experiment results show that the proposed hybrid system is efficient from memory and time computational point of views without compromising classification accuracy results.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125898199","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
A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands 基于遗传算法的延迟学习参数优化客户需求预测
2013 12th International Conference on Machine Learning and Applications Pub Date : 2013-12-04 DOI: 10.1109/ICMLA.2013.183
Mirko Kück, B. Scholz-Reiter
{"title":"A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands","authors":"Mirko Kück, B. Scholz-Reiter","doi":"10.1109/ICMLA.2013.183","DOIUrl":"https://doi.org/10.1109/ICMLA.2013.183","url":null,"abstract":"The prediction of time series is an important task both in academic research and in industrial applications. Firstly, an appropriate prediction method has to be chosen. Subsequently, the parameters of this prediction method have to be adjusted to the time series evolution. In particular, an accurate prediction of future customer demands is often difficult, due to several static and dynamic influences. As a promising prediction method, we propose a lazy learning algorithm based on phase space reconstruction and k-nearest neighbor search. This algorithm originates from chaos theory and nonlinear dynamics. In contrast to widely used linear prediction methods like the Box-Jenkins ARIMA method or exponential smoothing, this method is appropriate to reconstruct additional influences on the time series data and consider these influences within the prediction. However, in order to adjust the parameters of the prediction method to the observed time series evolution, a reasonable optimization algorithm is required. In this paper, we present a genetic algorithm for parameter optimization. In this way, the prediction method is automatically fitted accurately and quickly to observed time series data, in order to predict future values. The performance of the genetic algorithm is evaluated by an application to different time series of customer demands in production networks. The results show that the genetic algorithm is appropriate to find suitable parameter configurations. In addition, the prediction results indicate an improved forecasting accuracy of the proposed prediction algorithm compared to linear standard methods.","PeriodicalId":168867,"journal":{"name":"2013 12th International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129346310","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: 15
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信