Lea Canales, C. Strapparava, E. Boldrini, P. Martínez-Barco
The objective of this research is to develop a technique to automatically annotate emotional corpora. The complexity of automatic annotation of emotional corpora still presents numerous challenges and thus there is a need to develop a technique that allow us to tackle the annotation task. The relevance of this research is demonstrated by the fact that people's emotions and the patterns of these emotions provide a great value for business, individuals, society or politics. Hence, the creation of a robust emotion detection system becomes crucial. Due to the subjectivity of the emotions, the main challenge for the creation of emotional resources is the annotation process. Thus, with this staring point in mind, the objective of our paper is to illustrate an innovative and effective bootstrapping process for automatic annotations of emotional corpora. The evaluations carried out confirm the soundness of the proposed approach and allow us to consider the bootstrapping process as an appropriate approach to create resources such as an emotional corpus that can be employed on supervised machine learning towards the improvement of emotion detection systems.
{"title":"Exploiting a Bootstrapping Approach for Automatic Annotation of Emotions in Texts","authors":"Lea Canales, C. Strapparava, E. Boldrini, P. Martínez-Barco","doi":"10.1109/DSAA.2016.78","DOIUrl":"https://doi.org/10.1109/DSAA.2016.78","url":null,"abstract":"The objective of this research is to develop a technique to automatically annotate emotional corpora. The complexity of automatic annotation of emotional corpora still presents numerous challenges and thus there is a need to develop a technique that allow us to tackle the annotation task. The relevance of this research is demonstrated by the fact that people's emotions and the patterns of these emotions provide a great value for business, individuals, society or politics. Hence, the creation of a robust emotion detection system becomes crucial. Due to the subjectivity of the emotions, the main challenge for the creation of emotional resources is the annotation process. Thus, with this staring point in mind, the objective of our paper is to illustrate an innovative and effective bootstrapping process for automatic annotations of emotional corpora. The evaluations carried out confirm the soundness of the proposed approach and allow us to consider the bootstrapping process as an appropriate approach to create resources such as an emotional corpus that can be employed on supervised machine learning towards the improvement of emotion detection systems.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"56 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116372838","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}
The notion of roles is crucial in project management across various domains. A role indicates a broad set of tasks, activities, deliverables and responsibilities that the person needs to carry out within a project. Assigning roles to team members clarifies the expectations of work items to be delivered by each and structures the interactions of the team among themselves as well as with external stakeholders. This paper analyzes a sizeable real-life dataset regarding the actual usage of roles in software development and maintenance projects in a large multinational IT organization. The paper introduces and formalizes concepts such as seniority level of a role, career progression and career lines, formulates various business questions related to role-based project management, proposes analytics techniques to answer them and outlines the actual results produced to answer the business questions. The business questions are related to dependencies between roles, patterns in role assignments and durations, predicting role changes, discovering insights useful for meeting career aspirations, interesting role sequences etc. The proposed analytics algorithms are based on Markov models, sequence mining, classification and survival analysis.
{"title":"Role Models: Mining Role Transitions Data in IT Project Management","authors":"G. Palshikar, Sachin Pawar, Nitin Ramrakhiyani","doi":"10.1109/DSAA.2016.62","DOIUrl":"https://doi.org/10.1109/DSAA.2016.62","url":null,"abstract":"The notion of roles is crucial in project management across various domains. A role indicates a broad set of tasks, activities, deliverables and responsibilities that the person needs to carry out within a project. Assigning roles to team members clarifies the expectations of work items to be delivered by each and structures the interactions of the team among themselves as well as with external stakeholders. This paper analyzes a sizeable real-life dataset regarding the actual usage of roles in software development and maintenance projects in a large multinational IT organization. The paper introduces and formalizes concepts such as seniority level of a role, career progression and career lines, formulates various business questions related to role-based project management, proposes analytics techniques to answer them and outlines the actual results produced to answer the business questions. The business questions are related to dependencies between roles, patterns in role assignments and durations, predicting role changes, discovering insights useful for meeting career aspirations, interesting role sequences etc. The proposed analytics algorithms are based on Markov models, sequence mining, classification and survival analysis.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132364641","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}
Players' gameplay action-decision data can be used towards profiling as serious games analytics. The insights gained can help support the decisions for performance improvement and as 'prescriptions' for training – e.g., diagnosing who should receive training, how much training will be given, informing the design of the game, and determining the contents for inclusion and exclusion. Data-driven training prescription can help learning organizations save money by mitigating unnecessary training to reduce costs. Players' learning performance in games can be measured in lieu of their behaviors traced in situ the training environment. Novice players' action-decision data can first be converted into Course of Actions (COAs) before pairwise similarity comparison against that of the expert(s) to reveal how similar they are to the training goal, or expert/model answer. We identified three Gameplay Action-Decision (GAD) profiles from these gameplay action-decision data and applied them as diagnostics towards prescriptive training.
{"title":"Using Players' Gameplay Action-Decision Profiles to Prescribe Training: Reducing Training Costs with Serious Games Analytics","authors":"C. S. Loh, I. Li","doi":"10.1109/DSAA.2016.74","DOIUrl":"https://doi.org/10.1109/DSAA.2016.74","url":null,"abstract":"Players' gameplay action-decision data can be used towards profiling as serious games analytics. The insights gained can help support the decisions for performance improvement and as 'prescriptions' for training – e.g., diagnosing who should receive training, how much training will be given, informing the design of the game, and determining the contents for inclusion and exclusion. Data-driven training prescription can help learning organizations save money by mitigating unnecessary training to reduce costs. Players' learning performance in games can be measured in lieu of their behaviors traced in situ the training environment. Novice players' action-decision data can first be converted into Course of Actions (COAs) before pairwise similarity comparison against that of the expert(s) to reveal how similar they are to the training goal, or expert/model answer. We identified three Gameplay Action-Decision (GAD) profiles from these gameplay action-decision data and applied them as diagnostics towards prescriptive training.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134268936","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}
Jensen-Shannon divergence (JSD) does not provide adequate separation when the difference between input distributions is subtle. A recently introduced technique, Chisini Jensen Shannon Divergence (CJSD), increases JSD's ability to discriminate between probability distributions by reformulating with operators from Chisini mean. As a consequence, CJSDs also carry additional properties concerning robustness. The utility of this approach was validated in the form of two SVM kernels that give superior classification performance. Our work explores why the performance improvement to JSDs is afforded by this reformulation. We characterize the nature of this improvement based on the idea of relative dilation, that is how Chisini mean transforms JSD's range and prove a number of propositions that establish the degree of this separation. Finally, we provide empirical validation on a synthetic dataset that confirms our theoretical results pertaining to relative dilation.
{"title":"Dilation of Chisini-Jensen-Shannon Divergence","authors":"P. Sharma, Gary Holness","doi":"10.1109/DSAA.2016.25","DOIUrl":"https://doi.org/10.1109/DSAA.2016.25","url":null,"abstract":"Jensen-Shannon divergence (JSD) does not provide adequate separation when the difference between input distributions is subtle. A recently introduced technique, Chisini Jensen Shannon Divergence (CJSD), increases JSD's ability to discriminate between probability distributions by reformulating with operators from Chisini mean. As a consequence, CJSDs also carry additional properties concerning robustness. The utility of this approach was validated in the form of two SVM kernels that give superior classification performance. Our work explores why the performance improvement to JSDs is afforded by this reformulation. We characterize the nature of this improvement based on the idea of relative dilation, that is how Chisini mean transforms JSD's range and prove a number of propositions that establish the degree of this separation. Finally, we provide empirical validation on a synthetic dataset that confirms our theoretical results pertaining to relative dilation.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124535393","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}
Bernat Coma-Puig, J. Carmona, Ricard Gavaldà, Santiago Alcoverro, Victor Martin
Data from utility meters (gas, electricity, water) is a rich source of information for distribution companies, beyond billing. In this paper we present a supervised technique, which primarily but not only feeds on meter information, to detect meter anomalies and customer fraudulent behavior (meter tampering). Our system detects anomalous meter readings on the basis of models built using machine learning techniques on past data. Unlike most previous work, it can incrementally incorporate the result of field checks to grow the database of fraud and non-fraud patterns, therefore increasing model precision over time and potentially adapting to emerging fraud patterns. The full system has been developed with a company providing electricity and gas and already used to carry out several field checks, with large improvements in fraud detection over the previous checks which used simpler techniques.
{"title":"Fraud Detection in Energy Consumption: A Supervised Approach","authors":"Bernat Coma-Puig, J. Carmona, Ricard Gavaldà, Santiago Alcoverro, Victor Martin","doi":"10.1109/DSAA.2016.19","DOIUrl":"https://doi.org/10.1109/DSAA.2016.19","url":null,"abstract":"Data from utility meters (gas, electricity, water) is a rich source of information for distribution companies, beyond billing. In this paper we present a supervised technique, which primarily but not only feeds on meter information, to detect meter anomalies and customer fraudulent behavior (meter tampering). Our system detects anomalous meter readings on the basis of models built using machine learning techniques on past data. Unlike most previous work, it can incrementally incorporate the result of field checks to grow the database of fraud and non-fraud patterns, therefore increasing model precision over time and potentially adapting to emerging fraud patterns. The full system has been developed with a company providing electricity and gas and already used to carry out several field checks, with large improvements in fraud detection over the previous checks which used simpler techniques.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122785132","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}
Business Intelligence and the Kimball methodology, often referred to as dimensional modelling, are well established in data warehousing as a successful means of turning data into information. These techniques have been utilized in multiple business areas such as banking, manufacturing, marketing, sales, healthcare and more. Several articles have also shown how the Kimball approach can and has been used in the development of clinical research databases. However, these articles have also shown that there are weaknesses to the Kimball methodology when applied to complex areas such as clinical research. This paper describes our approach to address these weaknesses and meet the more sophisticated needs of health researchers by leveraging relationships within the underlying data and advanced techniques in the Kimball methodology.
{"title":"Meeting Health Care Research Needs in a Kimball Integrated Data Warehouse","authors":"R. Hart, A. Kuo","doi":"10.1109/DSAA.2016.91","DOIUrl":"https://doi.org/10.1109/DSAA.2016.91","url":null,"abstract":"Business Intelligence and the Kimball methodology, often referred to as dimensional modelling, are well established in data warehousing as a successful means of turning data into information. These techniques have been utilized in multiple business areas such as banking, manufacturing, marketing, sales, healthcare and more. Several articles have also shown how the Kimball approach can and has been used in the development of clinical research databases. However, these articles have also shown that there are weaknesses to the Kimball methodology when applied to complex areas such as clinical research. This paper describes our approach to address these weaknesses and meet the more sophisticated needs of health researchers by leveraging relationships within the underlying data and advanced techniques in the Kimball methodology.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123195470","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}
Counting the number of triangles in a large graph has many important applications in network analysis. Several frequently computed metrics such as the clustering coefficient and the transitivity ratio need to count the number of triangles. In this paper, we present a randomized framework for expressing and analyzing approximate triangle counting algorithms. We show that many existing approximate triangle counting algorithms can be described in terms of probability distributions given as parameters to the proposed framework. Then, we show that our proposed framework provides a quantitative measure for the quality of different approximate algorithms. Finally, we perform experiments on real-world networks from different domains and show that there is no unique sampling technique outperforming the others for all networks and the quality of sampling techniques depends on different factors such as the structure of the network, the vertex degree-triangle correlation and the number of samples.
{"title":"A Framework for Description and Analysis of Sampling-Based Approximate Triangle Counting Algorithms","authors":"M. H. Chehreghani","doi":"10.1109/DSAA.2016.15","DOIUrl":"https://doi.org/10.1109/DSAA.2016.15","url":null,"abstract":"Counting the number of triangles in a large graph has many important applications in network analysis. Several frequently computed metrics such as the clustering coefficient and the transitivity ratio need to count the number of triangles. In this paper, we present a randomized framework for expressing and analyzing approximate triangle counting algorithms. We show that many existing approximate triangle counting algorithms can be described in terms of probability distributions given as parameters to the proposed framework. Then, we show that our proposed framework provides a quantitative measure for the quality of different approximate algorithms. Finally, we perform experiments on real-world networks from different domains and show that there is no unique sampling technique outperforming the others for all networks and the quality of sampling techniques depends on different factors such as the structure of the network, the vertex degree-triangle correlation and the number of samples.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124229897","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}
M. Wojnowicz, Di Zhang, Glenn Chisholm, Xuan Zhao, M. Wolff
For very large datasets, random projections (RP) have become the tool of choice for dimensionality reduction. This is due to the computational complexity of principal component analysis. However, the recent development of randomized principal component analysis (RPCA) has opened up the possibility of obtaining approximate principal components on very large datasets. In this paper, we compare the performance of RPCA and RP in dimensionality reduction for supervised learning. In Experiment 1, study a malware classification task on a dataset with over 10 million samples, almost 100,000 features, and over 25 billion non-zero values, with the goal of reducing the dimensionality to a compressed representation of 5,000 features. In order to apply RPCA to this dataset, we develop a new algorithm called large sample RPCA (LS-RPCA), which extends the RPCA algorithm to work on datasets with arbitrarily many samples. We find that classification performance is much higher when using LS-RPCA for dimensionality reduction than when using random projections. In particular, across a range of target dimensionalities, we find that using LS-RPCA reduces classification error by between 37% and 54%. Experiment 2 generalizes the phenomenon to multiple datasets, feature representations, and classifiers. These findings have implications for a large number of research projects in which random projections were used as a preprocessing step for dimensionality reduction. As long as accuracy is at a premium and the target dimensionality is sufficiently less than the numeric rank of the dataset, randomized PCA may be a superior choice. Moreover, if the dataset has a large number of samples, then LS-RPCA will provide a method for obtaining the approximate principal components.
{"title":"Projecting \"Better Than Randomly\": How to Reduce the Dimensionality of Very Large Datasets in a Way That Outperforms Random Projections","authors":"M. Wojnowicz, Di Zhang, Glenn Chisholm, Xuan Zhao, M. Wolff","doi":"10.1109/DSAA.2016.26","DOIUrl":"https://doi.org/10.1109/DSAA.2016.26","url":null,"abstract":"For very large datasets, random projections (RP) have become the tool of choice for dimensionality reduction. This is due to the computational complexity of principal component analysis. However, the recent development of randomized principal component analysis (RPCA) has opened up the possibility of obtaining approximate principal components on very large datasets. In this paper, we compare the performance of RPCA and RP in dimensionality reduction for supervised learning. In Experiment 1, study a malware classification task on a dataset with over 10 million samples, almost 100,000 features, and over 25 billion non-zero values, with the goal of reducing the dimensionality to a compressed representation of 5,000 features. In order to apply RPCA to this dataset, we develop a new algorithm called large sample RPCA (LS-RPCA), which extends the RPCA algorithm to work on datasets with arbitrarily many samples. We find that classification performance is much higher when using LS-RPCA for dimensionality reduction than when using random projections. In particular, across a range of target dimensionalities, we find that using LS-RPCA reduces classification error by between 37% and 54%. Experiment 2 generalizes the phenomenon to multiple datasets, feature representations, and classifiers. These findings have implications for a large number of research projects in which random projections were used as a preprocessing step for dimensionality reduction. As long as accuracy is at a premium and the target dimensionality is sufficiently less than the numeric rank of the dataset, randomized PCA may be a superior choice. Moreover, if the dataset has a large number of samples, then LS-RPCA will provide a method for obtaining the approximate principal components.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115946938","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}
Lorne Swersky, Henrique O. Marques, J. Sander, R. Campello, A. Zimek
It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem. In this paper, we focus on the comparison of oneclass classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies in several important aspects. We study a number of one-class classification and unsupervised outlier detection methods in a rigorous experimental setup, comparing them on a large number of datasets with different characteristics, using different performance measures. Our experiments led to conclusions that do not fully agree with those of previous work.
{"title":"On the Evaluation of Outlier Detection and One-Class Classification Methods","authors":"Lorne Swersky, Henrique O. Marques, J. Sander, R. Campello, A. Zimek","doi":"10.1109/DSAA.2016.8","DOIUrl":"https://doi.org/10.1109/DSAA.2016.8","url":null,"abstract":"It has been shown that unsupervised outlier detection methods can be adapted to the one-class classification problem. In this paper, we focus on the comparison of oneclass classification algorithms with such adapted unsupervised outlier detection methods, improving on previous comparison studies in several important aspects. We study a number of one-class classification and unsupervised outlier detection methods in a rigorous experimental setup, comparing them on a large number of datasets with different characteristics, using different performance measures. Our experiments led to conclusions that do not fully agree with those of previous work.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130938419","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}
Oil and gas well production prediction takes place in early stages of production to estimate future recovery. A data driven workflow is proposed in this paper to construct a symbolic tree model to predict new well production using historic time-series production data of analogous wells. Production data are firstly aggregated and symbolized for dimensionality reduction and data discretization of time-series data. A symbolic tree is constructed on time-series symbol sequences, and pre-pruning mechanisms – minimum node size and spatial information gain – are integrated to achieve a compact and informative tree. A coverage index is used to assess the tree size. A case study was conducted applying the proposed workflow to shale gas wells in Montney-A pool in Canada. It has proved the feasibility and accuracy of the proposed method.
{"title":"A Symbolic Tree Model for Oil and Gas Production Prediction Using Time-Series Production Data","authors":"Bingjie Wei, Helen Pinto, Xin Wang","doi":"10.1109/DSAA.2016.36","DOIUrl":"https://doi.org/10.1109/DSAA.2016.36","url":null,"abstract":"Oil and gas well production prediction takes place in early stages of production to estimate future recovery. A data driven workflow is proposed in this paper to construct a symbolic tree model to predict new well production using historic time-series production data of analogous wells. Production data are firstly aggregated and symbolized for dimensionality reduction and data discretization of time-series data. A symbolic tree is constructed on time-series symbol sequences, and pre-pruning mechanisms – minimum node size and spatial information gain – are integrated to achieve a compact and informative tree. A coverage index is used to assess the tree size. A case study was conducted applying the proposed workflow to shale gas wells in Montney-A pool in Canada. It has proved the feasibility and accuracy of the proposed method.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131311408","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}
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