2013 IEEE International Conference on Granular Computing (GrC)最新文献_第3页

Algebraic analysis of statistical dependence 统计相关性的代数分析

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740426

S. Tsumoto, S. Hirano

引用次数: 0

Improvement in the Cohen-Sutherland line segment clipping algorithm Cohen-Sutherland线段裁剪算法的改进

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740399

Baoqing Jiang, Jingjing Han

引用次数: 5

Granular computing for inconsistent decision table 不一致决策表的粒度计算

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740393

Sujie Guan

引用次数: 1

An adaptive group recommender based on overlapping community detection 基于重叠社区检测的自适应群体推荐

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740444

Chen Yuan, Ting-jie Lv, Xia Chen

引用次数: 0

Defensive strategy of the goalkeeper based on the 3D vision and field division for the middle-size league of robocup 机器人世界杯中型联赛中基于三维视觉的守门员防守策略及场地划分

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740379

Jiehao Chen, Chen Ma, Zizhen Yan, Bo Chen, Yu Shen, Yu Liang

引用次数: 1

A new method of face recognition with data field and PCA 基于数据场和主成分分析的人脸识别新方法

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740429

Dakui Wang, Dongwei Li, Yi Lin

引用次数: 5

A novel detective and self-organized certificateless key management scheme in mobile ad hoc networks 一种新的移动自组织网络中检测和自组织的无证书密钥管理方案

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740452

Jun Zheng, Sanchun Xu, F. Zhao, Dianxin Wang, Yuanjun Li

引用次数: 7

Predicting movie sales revenue using online reviews 利用在线评论预测电影销售收入

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740443

Rui Yao, Jianhua Chen

{"title":"Predicting movie sales revenue using online reviews","authors":"Rui Yao, Jianhua Chen","doi":"10.1109/GrC.2013.6740443","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740443","url":null,"abstract":"With the rapid development of E-commerce, more and more online reviews for products and services are created, which form an important source of information for both sellers and customers. Research on sentiment and opinion mining for online review analysis has attracted increasingly more attention because such study helps leverage information from online reviews for potential economic impact. In this paper, we apply sentiment analysis and machine learning methods to study the relationship between the online reviews for a movie and the movie's box office revenue performance. We show that a simplified version of the sentiment-aware autoregressive model proposed in [5] can produce very good accuracy for predicting the box office sale using online review data. Our simplified version considers only positive and negative sentiments, and uses a very simple set of features with 14 affective key words for representing the sentiments in a review. In this way we obtain a simpler model which could be more efficient to train and use. Experiments indicate that the autoregressive model using both review sentiment data and the previous days' sale data results in higher accuracy than just using previous sale data alone. In addition, we create a classification model using Naïve Bayes Classifier for predicting the trend of the box office revenue from the review sentiment data.","PeriodicalId":415445,"journal":{"name":"2013 IEEE International Conference on Granular Computing (GrC)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114644808","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: 27

A Comparison of global and local probabilistic approximations in mining data with many missing attribute values 多属性值缺失数据挖掘中全局与局部概率近似的比较

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740384

Patrick G. Clark, J. Grzymala-Busse

{"title":"A Comparison of global and local probabilistic approximations in mining data with many missing attribute values","authors":"Patrick G. Clark, J. Grzymala-Busse","doi":"10.1109/GrC.2013.6740384","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740384","url":null,"abstract":"We present results of a novel experimental comparison of global and local probabilistic approximations. Global approximations are unions of characteristic sets while local approximations are constructed from blocks of attributevalue pairs. Two interpretations of missing attribute values are discussed: lost values and “do not care” conditions. Our main objective was to compare global and local probabilistic approximations in terms of the error rate. For our experiments we used six incomplete data sets with many missing attribute values. The best results were accomplished by global approximations (for two data sets), by local approximations (for one data set), and for the remaining three data sets the experiments ended with ties. Our next objective was to check the quality of non-standard probabilistic approximations, i.e., probabilistic approximations that were neither lower nor upper approximations. For four data sets the smallest error rate was accomplished by non-standard probabilistic approximations, for the remaining two data sets the smallest error rate was accomplished by upper approximations. Our final objective was to compare two interpretations of missing attribute values. For three data sets the best interpretation was the lost value, for one data set it was the “do not care” condition, for the remaining two cases there was a tie.","PeriodicalId":415445,"journal":{"name":"2013 IEEE International Conference on Granular Computing (GrC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127166275","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

A dynamic attribute reduction algorithm based on compound attribute measure 基于复合属性测度的动态属性约简算法

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740414

Wenbin Qian, Yonghong Xie, Bingru Yang

{"title":"A dynamic attribute reduction algorithm based on compound attribute measure","authors":"Wenbin Qian, Yonghong Xie, Bingru Yang","doi":"10.1109/GrC.2013.6740414","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740414","url":null,"abstract":"Attribute measure plays a vital role in the process of attribute reduction in decision systems. In spite of many attribute measures in heuristic attribute reduction algorithms can well evaluate the quality of attributes in decision systems, they do not consider the significance of information granularity beyond the positive region, such that some useful information not in the positive region may be loss in determining attribute quality. In addition, the attributes of decision systems usually vary dynamically with time in the real-world, correspondingly, attribute reduction needs updating to acquire new attribute reduct. In this paper, we firstly put forward a new compound attribute measure, which not only considers the measures of certain information in the positive region, but also considers the differences of information granularity of each attribute. Then based on the proposed compound attribute measure, we develop a dynamic attribute reduction algorithm for new reduct computation in dynamic decision systems. A case study is to illustrate the proposed reduction algorithm based on the compound attribute measure can find more useful attributes to guide the search for the best attribute reduct.","PeriodicalId":415445,"journal":{"name":"2013 IEEE International Conference on Granular Computing (GrC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125882609","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