2018 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第2页

Dominant-Set-Based Consensus For Fuzzy C-Means Clustering Ensemble 基于优势集的模糊c均值聚类合集一致性

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526927

P. Su, Tianhua Chen, Weifeng Xu, Xuqiang Shao, Hongtao Wang, Yitian Zhao

{"title":"Dominant-Set-Based Consensus For Fuzzy C-Means Clustering Ensemble","authors":"P. Su, Tianhua Chen, Weifeng Xu, Xuqiang Shao, Hongtao Wang, Yitian Zhao","doi":"10.1109/ICMLC.2018.8526927","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526927","url":null,"abstract":"Conventional clustering approaches partition a set of objects into a certain (some can automatically detect the number of clusters such as DBScan) number of clusters. During the partitioning process, the clusters of objects are produced where each object is assigned to one cluster. On the other hand, the dominantset-based clustering provides a formalisation of clusters by sequentially searching for individual clusters in the set of objects. The resultant clusters do not necessarily form a partition of the set. With the popularity of clustering ensemble, graph-based consensus approaches have been proposed with promising results achieved, many of which are based on the partition of the graph. In this paper, a dominant-set-based consensus method for fuzzy-c-means-based clustering ensemble is proposed. Different from traditional graph-based consensus techniques, the graph generated by the fuzzy clusters are grouped on the basis of the extracted dominant sets. The proposed approach employs a similarity relation to denote the links between component clusters from which the final clusters of ensemble are derived with the extracted dominant set. The proposed method is tested on benchmark data sets against several alternative ensemble methods for fuzzy c-means. The results of experiment show that the proposed dominant-set-based clustering ensemble method generally achieves higher accuracy than its competitors.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"122 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133684983","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

Detection Of Shilling Attack In Collaborative Filtering Recommender System By Pca And Data Complexity 基于Pca和数据复杂度的协同过滤推荐系统Shilling攻击检测

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526965

Fei Zhang, Zi-Jun Deng, Zhi-Min He, Xiao-Chuan Lin, Li-Li Sun

引用次数: 9

Vehicles Detection On Expressway Via Deep Learning: Single Shot Multibox Object Detector 基于深度学习的高速公路车辆检测:单镜头多盒目标检测器

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526958

Kuang-Hsuan Chen, T. Shou, John Kun-Han Li, Chun-Ming Tsai

引用次数: 6

ICMLC 2018 Copyright Page ICMLC 2018版权页面

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/icmlc.2018.8528252

引用次数: 0

Relative Confidence Based Information Fusion For Semg-Based Pattern Recognition 基于相对置信度的表面肌模式识别信息融合

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526931

Jinrong Li, Yinfeng Fang, Yong Ning, Jing Jie, Ping Tan, Honghai Liu

引用次数: 2

Research On Aquatic Products Safety Traceability System Based On Supply And Demand Network 基于供需网络的水产品安全追溯系统研究

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526950

Jing He, Wei-Xiang Liu

引用次数: 0

Image Quality Estimation Using Logarithmic Spread Transform Dither Modulation 基于对数扩频变换抖动调制的图像质量估计

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526945

Na Li, Xiaochen Yuan

引用次数: 0

Suspended Accounts: A Source of Tweets with Disgust and Anger Emotions for Augmenting Hate Speech Data Sample 暂停账户:一个带有厌恶和愤怒情绪的推文来源，用于增加仇恨言论数据样本

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527001

Wafa Alorainy, P. Burnap, Han Liu, Amir Javed, M. Williams

{"title":"Suspended Accounts: A Source of Tweets with Disgust and Anger Emotions for Augmenting Hate Speech Data Sample","authors":"Wafa Alorainy, P. Burnap, Han Liu, Amir Javed, M. Williams","doi":"10.1109/ICMLC.2018.8527001","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527001","url":null,"abstract":"In this paper we present a proposal to address the problem of the pricey and unreliable human annotation, which is important for detection of hate speech from the web contents. In particular, we propose to use the text that are produced from the suspended accounts in the aftermath of a hateful event as subtle and reliable source for hate speech prediction. The proposal was motivated after implementing emotion analysis on three sources of data sets: suspended, active and neutral ones, i.e. the first two sources of data sets contain hateful tweets from suspended accounts and active accounts, respectively, whereas the third source of data sets contain neutral tweets only. The emotion analysis indicated that the tweets from suspended accounts show more disgust, negative, fear and sadness emotions than the ones from active accounts, although tweets from both types of accounts might be annotated as hateful ones by human annotators. We train two Random Forest classifiers based on the semantic meaning of tweets respectively from suspended and active accounts, and evaluate the prediction accuracy of the two classifiers on unseen data. The results show that the classifier trained on the tweets from suspended accounts outperformed the one trained on the tweets from active accounts by 16% of overall F-score.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130420528","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

Surface Emg Channel Selection For Thumb Motion Classificationsignal 拇指运动分类信号的表面肌电信号通道选择

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526988

Wan-Fen Xu, Yinfeng Fang, Gongyue Zhang, Zhaojie Ju, Gongfa Li, Honghai Liu

引用次数: 3

Utilities Risk Indexing --- A Case for Person-to-Person (P2P) Debit and Credit Utilities 公用事业风险索引——个人对个人(P2P)借贷公用事业的案例

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/icmlc.2018.8527040

Xiaoming Zhou, W. Qian, Baifu Huang, Bo Xu, Anlong Su, Wangli Cui, Dawei Wang, Xiaolan Li, Fangyuan Xu

{"title":"Utilities Risk Indexing --- A Case for Person-to-Person (P2P) Debit and Credit Utilities","authors":"Xiaoming Zhou, W. Qian, Baifu Huang, Bo Xu, Anlong Su, Wangli Cui, Dawei Wang, Xiaolan Li, Fangyuan Xu","doi":"10.1109/icmlc.2018.8527040","DOIUrl":"https://doi.org/10.1109/icmlc.2018.8527040","url":null,"abstract":"P2P online loan platform is a new product with the development of Internet finance. Its new financial way can bring the participant both abundant rewards and high risk. Therefore, there are many risk assessing companies springing up. But common companies just forecast if a P2P online loan platform will be absconding according some characteristics and they can't explain the reason why the platform will be absconding in the future. Instead of the directly forecasting the future status of a platform, the risk of a platform absconding is more objective and the decision makers can completely consider a platform's interest, risk and other factors to make their own decision. After all, 51% risk of absconding is not the same as 91% risk of absconding, when the investors are thinking. In this model, the almost features of platforms including absconding and non-absconding will be considered and be disposed according to the type of the data. The feature candidates will be selected by different angles and methods. The forecast model will calculate risks using the input feature and there is a index in this paper to appraise how the model is good.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123385979","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