2018 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第8页

Impulse Noise Replacement With Adaptive Neighborhood Median Filtering 基于自适应邻域中值滤波的脉冲噪声替换

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527058

C. Hsieh, P. Huang, Qiangfu Zhao

引用次数: 2

Traffic Signals Timing Cycle Length Learning: Using Taxi Gps Trajectories 交通信号定时周期长度学习:使用出租车Gps轨迹

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526996

Qiong Yang, Jun Yu, Jian-min Han

引用次数: 3

J-Measure Based Pruning for Advancing Classification Performance of Information Entropy Based Rule Generation 基于j测度的剪枝提高基于信息熵的规则生成的分类性能

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527063

Han Liu, Ella Haig, Weili Ding

{"title":"J-Measure Based Pruning for Advancing Classification Performance of Information Entropy Based Rule Generation","authors":"Han Liu, Ella Haig, Weili Ding","doi":"10.1109/ICMLC.2018.8527063","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527063","url":null,"abstract":"Learning of classification rules is a popular approach of machine learning, which can be achieved through two strategies, namely divide-and-conquer and separate-and-conquer. The former is aimed at generating rules in the form of a decision tree, whereas the latter generates if-then rules directly from training data. From this point of view, the above two strategies are referred to as decision tree learning and rule learning, respectively. Both learning strategies can lead to production of complex rule based classifiers that overfit training data, which has motivated researchers to develop pruning algorithms towards reduction of overfitting. In this paper, we propose a J-measure based pruning algorithm, which is referred to as Jmean-pruning. The proposed pruning algorithm is used to advance the performance of the information entropy based rule generation method that follows the separate and conquer strategy. An experimental study is reported to show how Jmean-pruning can effectively help the above rule learning method avoid overfitting. The results show that the use of Jmean-pruning achieves to advance the performance of the rule learning method and the improved performance is very comparable or even considerably better than the one of C4.5.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"1 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":"129674444","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

Imbalanced Data Classification Based on Extreme Learning Machine Autoencoder 基于极限学习机自编码器的不平衡数据分类

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526934

C. Shen, Sufang Zhang, Jun-Hai Zhai, Ding-Sheng Luo, Jun-Fen Chen

引用次数: 11

Decision Support Based On Artificial Fish Swarm For Ship Collision Avoidance From Ais Data 基于人工鱼群的船舶避碰决策支持

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526933

Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang

{"title":"Decision Support Based On Artificial Fish Swarm For Ship Collision Avoidance From Ais Data","authors":"Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang","doi":"10.1109/ICMLC.2018.8526933","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526933","url":null,"abstract":"In the field of track detection and route monitoring problem of ECDIS, many artificial intelligence algorithms have been applied to avoid maritime accidents. To provide early warning and collision avoidance decision supporting of ship with ECDIS, one decisionsupport algorithm based on artificial fish swarm from AIS data is proposed. The AIS data has a great value for vessel traffic analyses, port calling information and all services related to vessel movements and their behavior. With historical and real-time AIS data, one data preprocessing step is first adopted to remove redundancy of raw data. Genetic algorithm and artificial fish swarm algorithm are then used to find an optimal safe avoidance turning angle, navigation restoration time and navigational restoration angle fortheshortest distance of collision avoidance from an economic viewpoint complying international regulations for preventing collisionsatsea (COLRGS) and the safety domain of a ship. Simulation results for head-on situation, overtaking situation and crossing situation based on ECDIS platform certifies that the algorithm can be used to optimize collision avoidance route effectively and gives an optimal collision avoidance route recommendation.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"30 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":"117252403","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

Improving Imbalanced Question Classification Using Structured Smote Based Approach 基于结构化Smote方法改进不平衡问题分类

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527028

Alaa Mohasseb, M. Bader-El-Den, Ella Haig, Han Liu

引用次数: 14

Application Research On Long Short-Term Memory Network In Fault Diagnosis 长短期记忆网络在故障诊断中的应用研究

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527031

Wei-Feng Wang, Xue-Huan Qiu, Cai-Sen Chen, Bo Lin, Hui-Min Zhang

引用次数: 17

Forecasting Results of Sport Events Through Deep Learning 基于深度学习的体育赛事结果预测

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526954

Shu-Hung Lin, Mu-Yen Chen, Hsiu-Sen Chiang

{"title":"Forecasting Results of Sport Events Through Deep Learning","authors":"Shu-Hung Lin, Mu-Yen Chen, Hsiu-Sen Chiang","doi":"10.1109/ICMLC.2018.8526954","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526954","url":null,"abstract":"The importance of competitive sport events such as the World Cup and the World Baseball Classic for a majority of people can be easily found through the heated discussions in newspapers and other types of media such as the Internet while the fad hits. They are also highly discussed topics. Many people are even one-day fans with the same expectations; that is, they want their team to win. It is, however, very difficult to determine which team will stand out among the many. In this study, records and data from the many contests that the National Basketball Association (NBA), which also deals with competitive sports, has held will be analyzed and discussed in order to forecast results of games. The deep learning approach will be adopted and convolutional neural networks and data from 4147 games over the past 3 years will be used for analysis and to facilitate training on and forecasts done applying the model. Finally, forecasting results will be discussed. In previous studies, convolutional neural networks were more frequently applied to identifying images or objects. Therefore, with the current study, the hope is to combine deep learning in the forecast of event results and that the approach helps add to the accuracy of forecast results compared to other classifiers.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"23 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":"125936970","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

Investigating The Influential Factors On Firefighter Injuries Using Statistical Machine Learning 利用统计机器学习研究消防员受伤的影响因素

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527021

Zijiang Yang, Youwu Liu

引用次数: 1

The Research Of Hierarchial Ip Trackback Technique 分层Ip回溯技术的研究

2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526949

Hong Zhang, Bing Guo, Yan Shen, Xuliang Duan, Yuncheng Shen, Xiangqian Dong

引用次数: 0