2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第6页

Predicting Employer Recruitment of Individuals with Autism Spectrum Disorders with Decision Trees 用决策树预测自闭症谱系障碍个体的雇主招聘

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00222

Kayleigh Hyde, A. Griffiths, Cristina Giannantonio, Amy E. HURLEY-HANSON, Erik J. Linstead

引用次数: 9

Multi-agent Reinforcement Learning Approach for Scheduling Cluster Tools with Condition Based Chamber Cleaning Operations 基于条件的腔室清洗操作集群工具调度多智能体强化学习方法

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00143

Cheolhui Hong, Tae-Eog Lee

引用次数: 9

Token-Based Adaptive Time-Series Prediction by Ensembling Linear and Non-linear Estimators: A Machine Learning Approach for Predictive Analytics on big Stock Data 集成线性和非线性估计量的基于标记的自适应时间序列预测:一种用于大股票数据预测分析的机器学习方法

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00242

K. J. Morris, S. Egan, Jorell L. Linsangan, C. Leung, A. Cuzzocrea, Calvin S. H. Hoi

{"title":"Token-Based Adaptive Time-Series Prediction by Ensembling Linear and Non-linear Estimators: A Machine Learning Approach for Predictive Analytics on big Stock Data","authors":"K. J. Morris, S. Egan, Jorell L. Linsangan, C. Leung, A. Cuzzocrea, Calvin S. H. Hoi","doi":"10.1109/ICMLA.2018.00242","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00242","url":null,"abstract":"With technological advancements, big data can be easily generated and collected in many applications. Embedded in these big data are useful information and knowledge that can be discovered by machine learning and data mining models, techniques or algorithms. A rich source of big data is stock exchange. The ability to effectively predict future stock prices improves the economic growth and development of a country. Traditional linear approaches for prediction (e.g., Kalman filters) may not be practical in handling big data like stock prices due to highly nonlinear and chaotic nature. This lead to the exploitation of various nonlinear estimators such as the extended Kalman filters, expert systems, and various neural network architectures. Moreover, to lessen the potential shortcomings of individual algorithms, ensemble approaches have been created by averaging values across different algorithms. Existing ensemble techniques mostly basket-together a collection of sample-based algorithms that are catered to nonlinear functions. To the best of our knowledge, traditional linear estimators have not yet been incorporated into such an ensemble. Hence, in this paper, we propose a machine learning (specifically, token-based ensemble) algorithm that utilizes both linear and nonlinear estimators to predict big financial time-series data. Our ensemble consists of a traditional Kalman filter, long short-term memory (LSTM) network, and the traditional linear regression model. We also explore the adaptive properties in short-term high-risk trading in the presence of noisy data like stock prices and demonstrate the performance of our ensemble.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"2 1","pages":"1486-1491"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88869593","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: 50

Deep Domain Adaptation to Predict Freezing of Gait in Patients with Parkinson's Disease 预测帕金森病患者步态冻结的深度域适应

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00163

Vishwas G. Torvi, Aditya R. Bhattacharya, Shayok Chakraborty

{"title":"Deep Domain Adaptation to Predict Freezing of Gait in Patients with Parkinson's Disease","authors":"Vishwas G. Torvi, Aditya R. Bhattacharya, Shayok Chakraborty","doi":"10.1109/ICMLA.2018.00163","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00163","url":null,"abstract":"Freezing of gait (FoG) is a common gait impairment in patients with advanced Parkinson's disease (PD), which manifests as sudden difficulties in starting or continuing locomotion. FoG often results in falls and negatively affect a patient's quality of life. Real-time detection algorithms have been developed, which detect FoG events using signals derived from wearable sensors. However, predicting FoG before it actually occurs opens the possibility of preemptive cueing, which can potentially avoid (or reduce the intensity and duration of) the episodes. Moreover, human gait involves significant subject-based variability and a machine learning model trained on a particular patient's data may not generalize well to other patients. In this paper, we study the performance of advanced deep learning algorithms to predict FoG events in short time durations before their occurrence. We further study the performance of domain adaptation (or transfer learning) algorithms to address the domain disparity between data from different subjects, in order to develop a better prediction model for a particular subject. To the best of our knowledge, this is the first research effort to study domain adaptation algorithms to predict FoG episodes in patients with PD. Our extensive empirical studies on a publicly available dataset (collected from 10 PD patients) demonstrate the potential of our algorithms to accurately identify FoG events before their onset. We believe this research will serve as a stepping stone toward the development of more advanced FoG prediction algorithms for patients with PD.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"52 1","pages":"1001-1006"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89373918","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: 39

CCDLC Detection Framework-Combining Clustering with Deep Learning Classification for Semantic Clones CCDLC检测框架——结合聚类和深度学习分类的语义克隆

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00111

Abdullah M. Sheneamer

引用次数: 7

Cognitive-Assisted Interactive Labeling of Skin Lesions and Blood Cells 认知辅助互动标记皮肤病变和血细胞

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00066

F. Luus, I. Akhalwaya, Naweed Khan

{"title":"Cognitive-Assisted Interactive Labeling of Skin Lesions and Blood Cells","authors":"F. Luus, I. Akhalwaya, Naweed Khan","doi":"10.1109/ICMLA.2018.00066","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00066","url":null,"abstract":"Supervised deep learning depends on labeled datasets to define objective categorization of subject matter, but annotation is typically quite expensive for specialized domains. The ISIC 2017 skin lesion and BCCD blood cell image datasets are used to represent complex medical annotation scenarios, where domain knowledge is not permitted in either preprocessing or feature extraction. A low complexity supervision method is proposed, based on an iterative machine learning algorithm that fulfills the requirements for cognitive-assisted labeling. The visualization and editing of feature spaces is demanded where new label information must be integrated to improve the embedding quality as feedback mechanism. The annotators ability for fast local homogeneity assessment is leveraged through compound labeling prospects, which is the basis for achieving efficient labeling. Improved unsupervised feature extraction is hypothesized to reduce the labeling burden so the best feature extractors are empirically located at the various depths in ImageNet-pretrained convolutional neural networks, including VGG-16, Inception-v4 and Inception-Resnet-v2. Annotator emulation is performed to simulate upper bounds of achievable labeling efficiency and to explore active learning dynamics. A two-fold increase in efficiency is shown in the case of partial labeling, despite the complexity of the skin lesion data and the marginal improvement with pretrained features.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"12 1","pages":"398-405"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87388133","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

Model Selection and Estimation of a Finite Shifted-Scaled Dirichlet Mixture Model 有限平移尺度Dirichlet混合模型的模型选择与估计

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00112

Rua Alsuroji, Nuha Zamzami, N. Bouguila

{"title":"Model Selection and Estimation of a Finite Shifted-Scaled Dirichlet Mixture Model","authors":"Rua Alsuroji, Nuha Zamzami, N. Bouguila","doi":"10.1109/ICMLA.2018.00112","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00112","url":null,"abstract":"This paper proposes an unsupervised learning algorithm for a finite mixture model of shifted-scaled Dirichlet distributions. Maximum likelihood and Newton raphson approaches are used for parameters estimation. In this research work, we address the flexibility challenge of the Dirichlet distribution by having another set of parameters for the location (beside the Scale parameter) that add functional probability models. This paper evaluates the capability of the discussed model to perform the categorization using both synthetic and real data related to the medical science to help in selecting wart treatment method, in the business field to detect the reasons behind employees' absenteeism, and the writer identification application to define the author of off-line handwritten documents. We also compare the model performance against scaled Dirichlet, the classic Dirichlet, and Gaussian mixture models. Finally, experimental results are presented on the selected datasets. Besides, we apply the minimum message length to determine the optimal number of the components found within each dataset.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"9 1","pages":"707-713"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87444382","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: 12

Xiao-Shih: The Educational Intelligent Question Answering Bot on Chinese-Based MOOCs 基于中文的mooc教育智能问答机器人

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00213

Hao-Hsuan Hsu, N. Huang

{"title":"Xiao-Shih: The Educational Intelligent Question Answering Bot on Chinese-Based MOOCs","authors":"Hao-Hsuan Hsu, N. Huang","doi":"10.1109/ICMLA.2018.00213","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00213","url":null,"abstract":"In this study, the educational intelligent question answering bot named Xiao-Shih has been developed for solving learners' questions as instructors and teaching assistants on MOOCs. Experiments were conducted with Xiao-Shih in a paid course titled \"Python for Data Science\" on \"ShareCourse\" which is one of the largest Chinese-based MOOC platform in Taiwan. Over one thousand discussion threads posted in both English and Chinese languages were retrieved to train the bot by natural language processing (NLP) and Random Forest (RF) in machine learning algorithms. This paper presents the implementation details on developing Xiao-Shih. First, we developed an initial version of Xiao-Shih with simply NLP techniques and text similarity approaches. However, Xiao-Shih only obtains 0.413 precision at best with different thresholds of the question similarity. Therefore, features and labels of answering correctness have been collected for the next version of Xiao-Shih. Trained by Random Forest with 70% of the entire dataset, Xiao-Shih obtains 0.833 precision with test dataset. With this educational intelligent question answering bot, learners can solve their problems immediately in seconds rather than wait for humans' response in hours even days. Moreover, Xiao-Shih can also ease instructors' and teaching assistants' burden on answering questions.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"119 1","pages":"1316-1321"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86183499","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

Applying Supervised Learning to the Static Prediction of Locality-Pattern Complexity in Scientific Code 将监督学习应用于科学码中位置模式复杂度的静态预测

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00162

Nasser Alsaedi, S. Carr, A. Fong

{"title":"Applying Supervised Learning to the Static Prediction of Locality-Pattern Complexity in Scientific Code","authors":"Nasser Alsaedi, S. Carr, A. Fong","doi":"10.1109/ICMLA.2018.00162","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00162","url":null,"abstract":"On modern computer systems, the performance of an application depends largely on its locality. Current compiler static locality analysis has limited applicability due to limited run-time information. By instrumenting and running programs, training-based locality analysis is able to predict the locality of an application based on the size of the input data accurately; however, it is costly in terms of time and space. In this paper, we combine source-code analysis with training-based locality analysis to construct a supervised-learning model parameterized only by the source code properties. This model is the first to be able to predict the upper bound of data reuse change (locality pattern complexity) at compile time for loop nests in array-based programs without the need to instrument and run the program. The result is the ability to predict how virtual memory usage grows as a function of the input size efficiently. We have evaluated our model using array-based code as input to a variety of classification algorithms. These algorithms include Naive Bayes, Decision tree, and Support Vector Machine (SVM). Our experiments show that SVM outperforms the other classifiers with 97% precision, a 97% true positive rate and a 1% false positive rate. We are able to predict the growth rate of memory usage in unseen scientific code accurately without the need to instrument and run the program. This work represents a significant step in developing an accurate static memory usage predictor for use in Virtual Machines (VMs) in cloud data centers.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"101 1","pages":"995-1000"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85978175","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: 2

Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection 基于标度律的制冷剂泄漏在线检测故障诊断方法

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00177

Shun Takeuchi, Takahiro Saito

{"title":"Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection","authors":"Shun Takeuchi, Takahiro Saito","doi":"10.1109/ICMLA.2018.00177","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00177","url":null,"abstract":"Early fault detection using instrumented sensor data is one of the promising application areas of machine learning in industrial facilities. However, it is difficult to improve the generalization performance of the trained fault-detection model because of the complex system configuration in the target diagnostic system and insufficient fault data. It is not trivial to apply the trained model to other systems. Here we propose a fault diagnosis method for refrigerant leak detection considering the physical modeling and control mechanism of an air-conditioning system. We derive a useful scaling law related to refrigerant leak. If the control mechanism is the same, the model can be applied to other air-conditioning systems irrespective of the system configuration. Small-scale off-line fault test data obtained in a laboratory are applied to estimate the scaling exponent. We evaluate the proposed scaling law by using real-world data. Based on a statistical hypothesis test of the interaction between two groups, we show that the scaling exponents of different air-conditioning systems are equivalent. In addition, we estimated the time series of the degree of leakage of real process data based on the scaling law and confirmed that the proposed method is promising for early leak detection through comparison with assessment by experts.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"13 1","pages":"1087-1094"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78443018","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