2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献_第5页

Alarm prediction in industrial machines using autoregressive LS-SVM models 基于自回归LS-SVM模型的工业机械报警预测

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008690

R. Langone, C. Alzate, A. Bey-Temsamani, J. Suykens

{"title":"Alarm prediction in industrial machines using autoregressive LS-SVM models","authors":"R. Langone, C. Alzate, A. Bey-Temsamani, J. Suykens","doi":"10.1109/CIDM.2014.7008690","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008690","url":null,"abstract":"In industrial machines different alarms are embedded in machines controllers. They make use of sensors and machine states to indicate to end-users various information (e.g. diagnostics or need of maintenance) or to put machines in a specific mode (e.g. shut-down when thermal protection is activated). More specifically, the alarms are often triggered based on comparing sensors data to a threshold defined in the controllers software. In batch production machines, triggering an alarm (e.g. thermal protection) in the middle of a batch production is crucial for the quality of the produced batch and results into a high production loss. This situation can be avoided if the settings of the production machine (e.g. production speed) is adjusted accordingly based on the temperature monitoring. Therefore, predicting a temperature alarm and adjusting the production speed to avoid triggering the alarm seems logical. In this paper we show the effectiveness of Least Squares Support Vector Machines (LS-SVMs) in predicting the evolution of the temperature in a steel production machine and, as a consequence, possible alarms due to overheating. Firstly, in an offline fashion, we develop a nonlinear autoregressive (NAR) model, where a systematic model selection procedure allows to carefully tune the model parameters. Afterwards, the NAR model is used online to forecast the future temperature trend. Finally, a classifier which uses as input the outcomes of the NAR model allows to foresee future alarms.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"520 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123066461","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: 7

Evaluating topic quality using model clustering 利用模型聚类评价主题质量

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008665

V. Mehta, R. Caceres, K. Carter

{"title":"Evaluating topic quality using model clustering","authors":"V. Mehta, R. Caceres, K. Carter","doi":"10.1109/CIDM.2014.7008665","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008665","url":null,"abstract":"Topic modeling continues to grow as a popular technique for finding hidden patterns, as well as grouping collections of new types of text and non-text data. Recent years have witnessed a growing body of work in developing metrics and techniques for evaluating the quality of topic models and the topics they generate. This is particularly true for text data where significant attention has been given to the semantic interpretability of topics using measures such as coherence. It has been shown however that topic assessments based on coherence metrics do not always align well with human judgment. Other efforts have examined the utility of information-theoretic distance metrics for evaluating topic quality in connection with semantic interpretability. Although there has been progress in evaluating interpretability of topics, the existing intrinsic evaluation metrics do not address some of the other aspects of concern in topic modeling such as: the number of topics to select, the ability to align topics from different models, and assessing the quality of training data. Here we propose an alternative metric for characterizing topic quality that addresses all three aforementioned issues. Our approach is based on clustering topics, and using the silhouette measure, a popular clustering index, for characterizing the quality of topics. We illustrate the utility of this approach in addressing the other topic modeling concerns noted above. Since this metric is not focused on interpretability, we believe it can be applied more broadly to text as well as non-text data. In this paper however we focus on the application of this metric to archival and non-archival text data.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"485 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123057660","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

Weighted one-class classification for different types of minority class examples in imbalanced data 不平衡数据中不同类型少数类样本的加权单类分类

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008687

B. Krawczyk, Michal Wozniak, F. Herrera

{"title":"Weighted one-class classification for different types of minority class examples in imbalanced data","authors":"B. Krawczyk, Michal Wozniak, F. Herrera","doi":"10.1109/CIDM.2014.7008687","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008687","url":null,"abstract":"Imbalanced classification is one of the most challenging machine learning problem. Recent studies show, that often the uneven ratio of objects in classes is not the biggest factor, determining the drop of classification accuracy. It is also related to some difficulties embedded in the nature of the data. In this paper we study the different types of minority class examples and distinguish four groups of objects - safe, borderline, rare and outliers. To deal with the imbalance problem, we use a one-class classification, that is focused on a proper identification of the minority class samples. We further augment this model by incorporating the knowledge about the minority object types in the training dataset. This is done applying weighted one-class classifier and adjusting weights assigned to minority class objects, depending on their type. A strategy for calculating the new weights for minority examples is proposed. Experimental analysis, carried on a set of benchmark datasets, confirms that the proposed model can achieve a satisfactory recognition rate and often outperform other state-of-the-art methods, dedicated to the imbalanced classification.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121546918","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: 27

Agglomerative hierarchical kernel spectral data clustering 聚类的层次核光谱数据聚类

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008142

Raghvendra Mall, R. Langone, J. Suykens

引用次数: 5

Predicting student success based on prior performance 根据先前的表现预测学生的成功

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008697

Ahmad Slim, G. Heileman, Jarred Kozlick, C. Abdallah

引用次数: 28

FATHOM: A neural network-based non-verbal human comprehension detection system for learning environments FATHOM:一个基于神经网络的非语言人类理解检测系统，用于学习环境

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008696

Fiona J. Buckingham, Keeley A. Crockett, Z. Bandar, J. O'Shea

{"title":"FATHOM: A neural network-based non-verbal human comprehension detection system for learning environments","authors":"Fiona J. Buckingham, Keeley A. Crockett, Z. Bandar, J. O'Shea","doi":"10.1109/CIDM.2014.7008696","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008696","url":null,"abstract":"This paper presents the application of FATHOM, a computerised non-verbal comprehension detection system, to distinguish participant comprehension levels in an interactive tutorial. FATHOM detects high and low levels of human comprehension by concurrently tracking multiple non-verbal behaviours using artificial neural networks. Presently, human comprehension is predominantly monitored from written and spoken language. Therefore, a large niche exists for exploring human comprehension detection from a non-verbal behavioral perspective using artificially intelligent computational models such as neural networks. In this paper, FATHOM was applied to a video-recorded exploratory study containing a learning task designed to elicit high and low comprehension states from the learner. The learning task comprised of watching a video on termites, suitable for the general public and an interview led question and answer session. This paper describes how FATHOM's comprehension classifier artificial neural network was trained and validated in comprehension detection using the standard backpropagation algorithm. The results show that high and low comprehension states can be detected from learner's non-verbal behavioural cues with testing classification accuracies above 76%.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132368629","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

What can spatial collectives tell us about their environment? 空间集体能告诉我们关于它们的环境的什么信息?

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008686

Zena Wood

{"title":"What can spatial collectives tell us about their environment?","authors":"Zena Wood","doi":"10.1109/CIDM.2014.7008686","DOIUrl":"https://doi.org/10.1109/CIDM.2014.7008686","url":null,"abstract":"Understanding how large groups of individuals move within their environment, and the social interactions that occur during this movement, is central to many fundamental interdisciplinary research questions; ranging from understanding the evolution of cooperation, to managing human crowd behaviour. If we could understand how groups of individuals interact with their environment, and any role that the environment plays in their behaviour, we could design and develop space to better suit their needs. Spatiotemporal datasets that record the movement of large groups of individuals are becoming increasingly available. A method, based on a set of coherence criteria, has previously been developed to identify different types of collective within such datasets. However, further investigations have revealed that the method can be used to reveal important information about the environment. This paper applies the method to a spatiotemporal dataset that records the movements of ships within the Solent, in the UK, over a twenty-four hour period to explore what can be inferred from the movement of groups of individuals, referred to as spatial collectives, regarding the environment.","PeriodicalId":117542,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132881906","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

Product aspect identification: Analyzing role of different classifiers 产品方面识别:不同分类器的作用分析

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008668

Xing Hu, S. Manna, Brian N. Truong

引用次数: 5

The use of process mining in a business process simulation context: Overview and challenges 在业务流程模拟上下文中使用流程挖掘:概述和挑战

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008693

Niels Martin, B. Depaire, A. Caris

引用次数: 17

Gender classification of subjects from cerebral blood flow changes using Deep Learning 利用深度学习从脑血流变化中对受试者进行性别分类

2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2014-12-01 DOI: 10.1109/CIDM.2014.7008672

T. Hiroyasu, K. Hanawa, U. Yamamoto

引用次数: 7