2019 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第3页

Multi-Channel Convolutional Neural Network for Targeted Sentiment Classification 面向目标情感分类的多通道卷积神经网络

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949286

Ting Yuan, Haihui Li, Hongya Zhao, Qianhua Cai, Han Liu, Xiaohui Hu

引用次数: 0

A Wearable Device of Salivation Detection and Improvement for Elderly at High Risk for Dysphagia 一种用于老年人吞咽困难高危人群唾液检测及改进的可穿戴设备

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949257

Chien-Nan Lee, Meng-Hsuan Shih, Chia-Wei Chen, Ding-Jiun Tzeng, Chuan-Che Shih, Yiu-Tong Chu, Ling Cheng

引用次数: 0

Utilization of the Infrared Image Capturing Combustion State for Estimating the Steam Flow Aming to Stabilize Garbage Power Generation 利用红外图像捕获燃烧状态估计蒸汽流量以稳定垃圾发电

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949303

T. Anjiki, K. Matsubayashi, Shunji Maeda

引用次数: 0

Deep Learning in Natural Language Processing: A State-of-the-Art Survey 深度学习在自然语言处理中的应用

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949185

J. Chai, Anming Li

引用次数: 27

Motion Control Design for Dynamic Spherical Mobile Robot via Fuzzy Control Approach 基于模糊控制方法的动态球形移动机器人运动控制设计

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949225

Wei-Fu Kao, Chun-Fei Hsu

引用次数: 3

Recognizing and Grading 3D Modeling Objects Using YOLO Based Deep Learning Network 基于YOLO的深度学习网络识别和分级3D建模对象

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949251

Hui-Hui Chen, Chiao-Wen Kao, B. Hwang, Kuo-Chin Fan

引用次数: 0

Distribution of Synthetic Populations of Japan for Social Scientists and Social Simulation Researchers 面向社会科学家和社会模拟研究人员的日本合成人口分布

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949245

T. Murata, Takuya Harada, Manabu Ichika Wa, Yusuke Goto, Lee Hao, S. Date, M. Munetomo, Akiyoshi Sugiki

引用次数: 0

Undersampling Near Decision Boundary for Imbalance Problems 不平衡问题的欠采样近决策边界

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949290

Jianjun Zhang, Ting Wang, Wing W. Y. Ng, Shuai Zhang, C. Nugent

{"title":"Undersampling Near Decision Boundary for Imbalance Problems","authors":"Jianjun Zhang, Ting Wang, Wing W. Y. Ng, Shuai Zhang, C. Nugent","doi":"10.1109/ICMLC48188.2019.8949290","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949290","url":null,"abstract":"Undersampling the dataset to rebalance the class distribution is effective to handle class imbalance problems. However, randomly removing majority examples via a uniform distribution may lead to unnecessary information loss. This would result in performance deterioration of classifiers trained using this rebalanced dataset. On the other hand, examples have different sensitivities with respect to class imbalance. Higher sensitivity means that this example is more easily to be affected by class imbalance, which can be used to guide the selection of examples to rebalance the class distribution and to boost the classifier performance. Therefore, in this paper, we propose a novel undersampling method, the UnderSampling using Sensitivity (USS), based on sensitivity of each majority example. Examples with low sensitivities are noisy or safe examples while examples with high sensitivities are borderline examples. In USS, majority examples with higher sensitivities are more likely to be selected. Experiments on 20 datasets confirm the superiority of the USS against one baseline method and five resampling methods.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130559211","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: 14

An Acceleration Method for Computing Dominace Classes in Ordered Information System 有序信息系统中支配类计算的一种加速方法

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949318

Yan Li, Jing Zhang, Qiang He, Siyuan Liu, Lujing Huo

{"title":"An Acceleration Method for Computing Dominace Classes in Ordered Information System","authors":"Yan Li, Jing Zhang, Qiang He, Siyuan Liu, Lujing Huo","doi":"10.1109/ICMLC48188.2019.8949318","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949318","url":null,"abstract":"In rough set theory, two crisp sets (i.e., the lower and upper approximates of a target concept) is used to describe uncertainties in given information systems. However, the traditional rough set models are built based on equivalence relations which do not consider the preference relationship of attribute values. Dominance relation-based rough set approach effectively solve this problem which uses dominance relations to substitute equivalence relations to deal with ordered data. In this kind of approach, the computing of dominance class is a necessary step to attribute reduction which is very time-consuming. In order to reduce the computational cost in calculating dominance classes, this paper presents a method to compute dominance classes by gradually reducing the search space in the domain. The corresponding algorithm is proposed. In each step of the algorithm, the inferior classes of the objects in a given information system are removed in the universe with the increase of the attributes. Experiments using six UCI data show that the proposed method improves the efficiency of computing dominance classes with the increasing of attributes and objects.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126793686","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

Efficient Binocular Stereo Matching Based on Sad and Improved Census Transformation 基于Sad和改进的人口普查转换的高效双目立体匹配

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949324

Yun Zhang, Wenxiang Chen, Han Liu, Jinhua Liu, Hui Du

引用次数: 1