2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第8页

Classification of Breast Cancer Risk Factors Using Several Resampling Approaches 几种重采样方法对乳腺癌危险因素的分类

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00202

Md Faisal Kabir, Simone A. Ludwig

{"title":"Classification of Breast Cancer Risk Factors Using Several Resampling Approaches","authors":"Md Faisal Kabir, Simone A. Ludwig","doi":"10.1109/ICMLA.2018.00202","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00202","url":null,"abstract":"Breast cancer is the most common cancer in women worldwide and the second most common cancer overall. Predicting the risk of breast cancer occurrence is an important challenge for clinical oncologists as it has direct influence in daily practice and clinical service. Classification is one of the supervised learning models that is applied in medical domains. Achieving better performance on real data that contains imbalance characteristics is a very challenging task. Machine learning researchers have been using various techniques to obtain higher accuracy, generally by correctly identifying majority class samples while ignoring the instances of the minority class. However, in most of the cases the concept of the minority class instances usually is of higher interest than the majority class. In this research, we applied three different classification techniques on a real world breast cancer risk factors data set. First, we applied specified classification techniques on breast cancer data without applying any resampling technique. Second, since the data is imbalanced meaning data has an unequal distribution between the classes, we applied several resampling methods to get better performance before applying the classifiers. The experimental results show significant improvement on using a resampling method as compared to applying no resampling technique, particularly for the minority class.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"8 1","pages":"1243-1248"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88837153","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

Sinkhorn Divergence of Topological Signature Estimates for Time Series Classification 时间序列分类拓扑特征估计的Sinkhorn散度

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00113

C. Stephen

引用次数: 0

Neural Fingerprint Enhancement 神经指纹增强

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00025

Edward Raff

引用次数: 4

Prediction of Ejection State for a Pneumatic Valve-Controlled Micro-Droplet Generator by a BP Neural Network 基于BP神经网络的气动阀控微液滴发生器弹射状态预测

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00159

Fei Wang, Jiangeng Li, Yiwei Wang, Weijie Bao, Z. Er, Xiaoyi Wang, Keyan Ren, Zhihai Wang

{"title":"Prediction of Ejection State for a Pneumatic Valve-Controlled Micro-Droplet Generator by a BP Neural Network","authors":"Fei Wang, Jiangeng Li, Yiwei Wang, Weijie Bao, Z. Er, Xiaoyi Wang, Keyan Ren, Zhihai Wang","doi":"10.1109/ICMLA.2018.00159","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00159","url":null,"abstract":"Pneumatic valve-controlled micro-droplet generation is a printing technique that has potential applications in many fields, especially in the field of biomedical printing. The droplet generation is controlled by a solenoid valve being briefly turned on, so that high pressure gas enters the liquid reservoir, forming a gas pressure pulse P(t), forcing the liquid out through a tiny nozzle to form a micro-droplet. Under the typical working conditions, P(t) is not consistent. Since P(t) is highly correlated with the micro-droplet ejection state, the inconsistency of P(t) results in fluctuation of ejection state. For each injection, the P(t) is acquired by a high speed pressure sensor, and the ejection state is obtained by machine vision processing. A machine learning method based on BP neural network is used to establish a prediction model with P(t) as the input and the droplet ejection state as the output. Experiments show that a BP neural network with only a single hidden layer and two neurons can accurately predict the number of droplets with an accuracy higher than 99%. Another experiment shows that a more complex double hidden layer BP neural network can improve the prediction accuracy for the position of droplets after a certain time delay. In summary, through pressure pulse P(t), the predictive model established by the machine learning method can effectively predict the micro-droplet ejection state. This technique may be used for real time monitoring and control of the pneumatic valve-controlled micro-droplet generator.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"18 3","pages":"977-982"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91489580","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

LiveFace: A Multi-task CNN for Fast Face-Authentication LiveFace:用于快速人脸认证的多任务CNN

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00155

Xiaowen Ying, Xin Li, M. Chuah

{"title":"LiveFace: A Multi-task CNN for Fast Face-Authentication","authors":"Xiaowen Ying, Xin Li, M. Chuah","doi":"10.1109/ICMLA.2018.00155","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00155","url":null,"abstract":"Modern face recognition systems are accurate but they are vulnerable to different types of spoofing attacks. To solve this problem, conventional face authentication systems typically employ an additional module to analyze the liveness of the input faces before feeding it into the face recognition module. Such two-stage designs not only suffer from longer processing time but also require more storage and resources, which are usually limited on mobile and embedded platforms. In this paper, we propose a multi-task Convolutional Neural Network(CNN), namely LiveFace, for face-authentication. Given an input face image, LiveFace generates two outputs through a single stage: (i) a face representation that can be used for identification or verification, and (ii) the corresponding liveness score. The two tasks share lower layers to reduce the computation cost. Experimental results using three datasets show that our model achieves a comparable performance on both face recognition and anti-spoofing tasks but much faster than conventional authentication systems. In addition, we have implemented a prototype of our scheme on Android phones and demonstrated that our scheme can run in real-time on three Android devices that we have tested.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"42 1","pages":"955-960"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91498697","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: 8

Training Generative Adversarial Networks with Bidirectional Backpropagation 基于双向反向传播的生成对抗网络训练

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00190

Olaoluwa Adigun, B. Kosko

{"title":"Training Generative Adversarial Networks with Bidirectional Backpropagation","authors":"Olaoluwa Adigun, B. Kosko","doi":"10.1109/ICMLA.2018.00190","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00190","url":null,"abstract":"Training generative adversarial networks with the new bidirectional backpropagation algorithm improved performance compared with ordinary unidirectional backpropagation. Bidirectional backpropagation trains a multilayer neural network in the backward direction as well as in the forward direction over the same weights and neurons. The result approximates a set-level inverse mapping that tends to improve the learning of the forward classification mapping. We compared bidirectional backpropagation training of the discriminator with unidirectional training for the standard vanilla GAN on MNIST data and a deep convolutional GAN on CIFAR-10 image data. We also compared B-BP and unidirectional training for a Wasserstein GAN on both MNIST and CIFAR-10 data. Bidirectional training substantially improved the inception score of the vanilla GAN's generated digit images for MNIST data. It increased the vanilla GAN's inception score by 22.3% and greatly reduced the GAN's incidence of mode collapse. Bidirectional training improved the inception score of the deep-convolutional GAN's generated samples by 3.3% on the CIFAR-10 data set. Bidirectional training also increased the Wasserstein GAN's inception score by 4.4% on the MNIST data and by 10.0% on the CIFAR-10 image data.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"10 1","pages":"1178-1185"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91554932","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: 8

Use Online Dictionary Learning to Get Parts-Based Decomposition of Noisy Data 使用在线词典学习获得基于部件的噪声数据分解

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00243

Daming Lu

引用次数: 1

Deep Reinforcement Learning Monitor for Snapshot Recording 快照记录的深度强化学习监视器

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00095

Giang Dao, Indrajeet Mishra, Minwoo Lee

引用次数: 10

Fine Object Detection in Automated Solar Panel Layout Generation 太阳能板布局自动生成中的精细目标检测

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00228

Shantanu Deshmukh, Teng-Sheng Moh

引用次数: 11

Learning Generative Models of Social Interactions with Humans-in-the-Loop 人类在循环中学习社会互动的生成模型

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00082

Dan Feng, P. Sequeira, Elín Carstensdóttir, M. S. El-Nasr, S. Marsella

{"title":"Learning Generative Models of Social Interactions with Humans-in-the-Loop","authors":"Dan Feng, P. Sequeira, Elín Carstensdóttir, M. S. El-Nasr, S. Marsella","doi":"10.1109/ICMLA.2018.00082","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00082","url":null,"abstract":"The development of agents that can engage in human social interaction has become critical in an increasingly wide range of applications. The focus of this work is modeling agents for social skills training where learners can interact with the autonomous characters in social scenarios and thereby acquire skills that can be applied in the real world. A key goal in the design of these systems is to allow users to explore various actions and tactics while still having the agents generate consistent and diverse responses. Providing the ability to explore different tactics raises a significant content challenge for the design of agents. To tackle the creative content creation problem, this paper introduces a humans-in-the-loop iterative process to automatically generate rich, varied content from a small amount of vignettes provided by online crowd workers. Specifically, this process uses the crowd to iteratively refine and improve an ensemble of generative models. The results show that the iterative, ensemble based approach generates more coherent and novel interactions than alternative non-ensemble, non-iterative approaches. The results presented in this paper can potentially provide the basis for flexible agent-based training systems.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"28 1","pages":"509-516"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81394693","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