2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)最新文献_第7页

Hypergraph Link Prediction: Learning Drug Interaction Networks Embeddings 超图链接预测:学习药物相互作用网络嵌入

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00299

M. Vaida, Kevin Purcell

{"title":"Hypergraph Link Prediction: Learning Drug Interaction Networks Embeddings","authors":"M. Vaida, Kevin Purcell","doi":"10.1109/ICMLA.2019.00299","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00299","url":null,"abstract":"Graph neural networks (GNNs) have revolutionized deep learning on non-Euclidean data domains, and are extensively used in fields such as social media and recommendation systems. However, complex relational data structures such as hypergraphs, pose challenges for GNNs in terms of their ability to model, embed, and learn relational complexities of multigraphs. Most GNNs focus on capturing flat local neighborhoods of a node thus failing to account for structural properties of multi-relational graphs. This paper introduces Hypergraph Link Prediction (HLP), a novel approach of encoding the multilink structure of graphs. HLP allows pooling operations to incorporate a 360 degrees overview of a node interaction profile, by learning local neighborhood and global hypergraph structure simultaneously. Global graph information is injected into node representations, such that unique global structural patterns of every node are encoded at the node level. HLP leverages the augmented hypergraph adjacency matrix to incorporate the depth of the hypergraph in the convolutional layers. The model is applied to the task of predicting multi-drug interactions, by modeling relations between pairs of drugs as a hypergraph. The existence and the type of drug interactions between the same pair of drugs are mapped as multiple edges, and can be inferred by learning the multigraph local and global structure concurrently. To account for molecular graph properties of a drug, additional drug chemical graph structural fingerprints are included as node attributes.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132927363","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

Stochastic Coordinate Descent for 01 Loss and Its Sensitivity to Adversarial Attacks 01损失的随机坐标下降及其对抗性攻击的敏感性

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00056

Meiyan Xie, Yunzhe Xue, Usman Roshan

{"title":"Stochastic Coordinate Descent for 01 Loss and Its Sensitivity to Adversarial Attacks","authors":"Meiyan Xie, Yunzhe Xue, Usman Roshan","doi":"10.1109/ICMLA.2019.00056","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00056","url":null,"abstract":"The 01 loss while hard to optimize is least sensitive to outliers compared to its continuous differentiable counterparts, namely hinge and logistic loss. Recently the 01 loss has been shown to be most robust compared to surrogate losses against corrupted labels which can be interpreted as adversarial attacks. Here we propose a stochastic coordinate descent heuristic for linear 01 loss classification. We implement and study our heuristic on real datasets from the UCI machine learning archive and find our method to be comparable to the support vector machine in accuracy and tractable in training time. We conjecture that the 01 loss may be harder to attack in a black box setting due to its non-continuity and infinite solution space. We train our linear classifier in a one-vs-one multi-class strategy on CIFAR10 and STL10 image benchmark datasets. In both cases we find our classifier to have the same accuracy as the linear support vector machine but more resilient to black box attacks. On CIFAR10 the linear support vector machine has 0% on adversarial examples while the 01 loss classifier hovers about 10%. On STL10 the linear support vector machine has 0% accuracy whereas 01 loss is at 10%. Our work here suggests that 01 loss may be more resilient to adversarial attacks than the hinge loss and further work is required.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117020413","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

Deep Learning and Thresholding with Class-Imbalanced Big Data 类不平衡大数据下的深度学习与阈值

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00134

Justin M. Johnson, T. Khoshgoftaar

{"title":"Deep Learning and Thresholding with Class-Imbalanced Big Data","authors":"Justin M. Johnson, T. Khoshgoftaar","doi":"10.1109/ICMLA.2019.00134","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00134","url":null,"abstract":"Class imbalance is a regularly occurring problem in machine learning that has been studied extensively over the last two decades. Various methods for addressing class imbalance have been introduced, including algorithm-level methods, datalevel methods, and hybrid methods. While these methods are well studied using traditional machine learning algorithms, there are relatively few studies that explore their application to deep neural networks. Thresholding, in particular, is rarely discussed in the deep learning with class imbalance literature. This paper addresses this gap by conducting a systematic study on the application of thresholding with deep neural networks using a Big Data Medicare fraud data set. We use random oversampling (ROS), random under-sampling (RUS), and a hybrid ROS-RUS to create 15 training distributions with varying levels of class imbalance. With the fraudulent class size ranging from 0.03%–60%, we identify optimal classification thresholds for each distribution on random validation sets and then score the thresholds on a 20% holdout test set. Through repetition and statistical analysis, confidence intervals show that the default threshold is never optimal when training data is imbalanced. Results also show that the optimal threshold outperforms the default threshold in nearly all cases, and linear models indicate a strong linear relationship between the minority class size and the optimal decision threshold. To the best of our knowledge, this is the first study to provide statistical results that describe optimal classification thresholds for deep neural networks over a range of class distributions.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114950196","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: 15

Deep Ensemble Network for Quantification and Severity Assessment of Knee Osteoarthritis 用于膝骨关节炎量化和严重程度评估的深度集成网络

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00163

Mohammed Bany Muhammad, A. Moinuddin, M. Lee, Yanfei Zhang, V. Abedi, R. Zand, M. Yeasin

{"title":"Deep Ensemble Network for Quantification and Severity Assessment of Knee Osteoarthritis","authors":"Mohammed Bany Muhammad, A. Moinuddin, M. Lee, Yanfei Zhang, V. Abedi, R. Zand, M. Yeasin","doi":"10.1109/ICMLA.2019.00163","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00163","url":null,"abstract":"The assessment of knee joint gap and severity of Osteoarthritis (OA) is subjective and often inaccurate. The main source of error is due to the judgement of human expert from low resolution images (i.e., X-ray images). To address the problem, we developed an ensemble of Deep Learning (DL) model to objectively score the severity of OA only from the radiometric images. The proposed method consists of two main modules. First, we developed a scale invariant and aspect ratio preserving automatic localization and characterization of the kneecap area. Second, we developed multiple instances of \"hyper parameter optimized\" DL models and fused them using ensemble classification to score the severity of OA. In this implementation, we used three convolutional neural networks to improve the bias-variance trade-off, and boost accuracy and generalization. We tested our modeling framework using a collection of 4,796 X-ray images from Osteoarthritis Initiative (OAI). Our results show a higher performance (~ 2-8%) when compared to the state-of-the-art methods. Finally, this machine learning-based methodology provides a pipeline in decision support system for assessing and quantifying the OA severity.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115078464","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

A Deep Learning Approach to Distributed Anomaly Detection for Edge Computing 边缘计算分布式异常检测的深度学习方法

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00169

Okwudili M. Ezeme, Q. Mahmoud, Akramul Azim

{"title":"A Deep Learning Approach to Distributed Anomaly Detection for Edge Computing","authors":"Okwudili M. Ezeme, Q. Mahmoud, Akramul Azim","doi":"10.1109/ICMLA.2019.00169","DOIUrl":"https://doi.org/10.1109/ICMLA.2019.00169","url":null,"abstract":"One of the multiplier effects of the boom in mobile technologies ranging from cell phones to computers and wearables like smart watches is that every public and private common spaces are now dotted with Wi-Fi hotspots. These hotspots provide the convenience of accessing the internet on-the-go for either play or work. Also, with the increased automation of our daily routines by our mobile devices via a multitude of applications, our vulnerability to cyber fraud or attacks becomes higher too. Hence, the need for heightened security that is capable of detecting anomalies on-the-fly. However, these edge devices connected to the local area network come with diverse capabilities with varying degrees of limitations in compute and energy resources. Therefore, running a process-based anomaly detector is not given a high priority in these devices because; a) the primary functions of the applications running on the devices is not security; therefore, the device allocates much of its resources into satisfying the primary duty of the applications. b) the volume and velocity of the data are high. Therefore, in this paper, we introduce a multi-node (nodes and devices are used interchangeably in the paper) ad-hoc network that uses a novel offloading scheme to bring an online anomaly detection capability on the kernel events to the nodes in the network. We test the framework in a Wi-Fi-based ad-hoc network made up of several devices, and the results confirm our hypothesis that the scheme can reduce latency and increase the throughput of the anomaly detector, thereby making online anomaly detection in the edge possible without sacrificing the accuracy of the deep recurrent neural network.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115715692","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: 3

IoT Environmental Analyzer using Sensors and Machine Learning for Migraine Occurrence Prevention 使用传感器和机器学习预防偏头痛的物联网环境分析仪

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00239

Rosemarie J. Day, H. Salehi, Mahsa Javadi

引用次数: 2

Widened Learning of Index Tracking Portfolios 指数跟踪投资组合的拓宽学习

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00291

Iuliia Gavriushina, Oliver R. Sampson, M. Berthold, W. Pohlmeier, C. Borgelt

引用次数: 0

Forcing Interpretability for Deep Neural Networks through Rule-Based Regularization 基于规则的正则化深度神经网络的强制可解释性

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00126

Nadia Burkart, Marco F. Huber, Phillip Faller

引用次数: 3

Deep Neuronal Based Classifiers for Wireless Multi-hop Network Mobility Models 基于深度神经元的无线多跳网络移动模型分类器

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00111

Daniel Gutiérrez, S. Toral

引用次数: 2

Low-Bit Quantization and Quantization-Aware Training for Small-Footprint Keyword Spotting 小尺寸关键字识别的低比特量化和量化感知训练

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI: 10.1109/ICMLA.2019.00127

Yuriy Mishchenko, Yusuf Goren, Ming Sun, Chris Beauchene, Spyros Matsoukas, Oleg Rybakov, S. Vitaladevuni

引用次数: 19