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2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

17-19 Oct. 2016

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Displaying Results 1 - 25 of 96
  • [Front cover]

    Publication Year: 2016, Page(s): c1
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  • [Title page i]

    Publication Year: 2016, Page(s): i
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  • [Title page iii]

    Publication Year: 2016, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2016, Page(s): iv
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  • Table of contents

    Publication Year: 2016, Page(s):v - xii
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  • Organizing Committee

    Publication Year: 2016, Page(s):xiii - xvi
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  • Program Committee

    Publication Year: 2016, Page(s):xvii - xx
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  • Reviewers

    Publication Year: 2016, Page(s): xxi
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  • On the Evaluation of Outlier Detection and One-Class Classification Methods

    Publication Year: 2016, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (185 KB) | HTML iconHTML

    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 one-class 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 d... View full abstract»

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  • Active Semi-Supervised Classification Based on Multiple Clustering Hierarchies

    Publication Year: 2016, Page(s):11 - 20
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (473 KB) | HTML iconHTML

    Active semi-supervised learning can play an important role in classification scenarios in which labeled data are difficult to obtain, while unlabeled data can be easily acquired. This paper focuses on an active semi-supervised algorithm that can be driven by multiple clustering hierarchies. If there is one or more hierarchies that can reasonably align clusters with class labels, then a few queries... View full abstract»

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  • Combining Static and Dynamic Features for Multivariate Sequence Classification

    Publication Year: 2016, Page(s):21 - 30
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (747 KB) | HTML iconHTML

    Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dictate which classification method can be applied as most of the machine learning algorithms are designed to deal with either one or another type of data. In real-life scenarios, however, it is often the case that both stat... View full abstract»

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  • Correcting Relational Bias to Improve Classification in Sparsely-Labeled Networks

    Publication Year: 2016, Page(s):31 - 40
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (205 KB) | HTML iconHTML

    Many classification problems involve nodes that have a natural connection between them, such as links between people, pages, or social network accounts. Recent work has demonstrated how to learn relational dependencies from these links, then leverage them as predictive features. However, while this can often improve accuracy, the use of linked information can also lead to cascading prediction erro... View full abstract»

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  • Hyperparameter Optimization Machines

    Publication Year: 2016, Page(s):41 - 50
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (320 KB) | HTML iconHTML

    Algorithm selection and hyperparameter tuning are omnipresent problems for researchers and practitioners. Hence, it is not surprising that the efforts in automatizing this process using various meta-learning approaches have been increased. Sequential model-based optimization (SMBO) is ne of the most popular frameworks for finding optimal hyperparameter configurations. Originally designed for black... View full abstract»

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  • Temporal Network Change Detection Using Network Centralities

    Publication Year: 2016, Page(s):51 - 60
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (865 KB) | HTML iconHTML

    In this paper, we propose a novel change detection method for temporal networks. In usual change detection algorithms, change scores are generated from an observed time series. When this change score reaches a threshold, an alert is raised to declare the change. Our method aggregates these change scores and alerts based on network centralities. Many types of changes in a network can be discovered ... View full abstract»

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  • Harvester: Influence Optimization in Symmetric Interaction Networks

    Publication Year: 2016, Page(s):61 - 70
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1098 KB) | HTML iconHTML

    The problem of optimizing influence diffusion in a network has applications in areas such as marketing, disease control, social media analytics, and more. In all cases, an initial set of influencers are chosen so as to optimize influence propagation.While a lot of research has been devoted to the influence maximization problem, most solutions proposed to date apply on directed networks, considerin... View full abstract»

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  • Pattern Matching Trajectories for Investigative Graph Searches

    Publication Year: 2016, Page(s):71 - 79
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (675 KB) | HTML iconHTML

    Investigative graph search is the process of searching for and prioritizing entities of interest that may exhibit part or all of a pattern of attributes or connections for a latent behavior. In this work we formulate a related sub-problem of determining the pattern matching trajectories of such entities. The goal is to not only provide analysts with the ability to find full or partial matches agai... View full abstract»

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  • A Framework for Description and Analysis of Sampling-Based Approximate Triangle Counting Algorithms

    Publication Year: 2016, Page(s):80 - 89
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (214 KB) | HTML iconHTML

    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 t... View full abstract»

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  • Limiting the Diffusion of Information by a Selective PageRank-Preserving Approach

    Publication Year: 2016, Page(s):90 - 99
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (482 KB) | HTML iconHTML

    The problem of limiting the diffusion of information in social networks has received substantial attention. To deal with the problem, existing works aim to prevent the diffusion of information to as many nodes as possible, by deleting a given number of edges. Thus, they assume that the diffusing information can affect all nodes and that the deletion of each edge has the same impact on the informat... View full abstract»

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  • An Exploratory Statistical Cusp Catastrophe Model

    Publication Year: 2016, Page(s):100 - 109
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (319 KB) | HTML iconHTML

    The Cusp Catastrophe Model provides a promising approach for health and behavioral researchers to investigate both continuous and quantum changes in one modeling framework. However, application of the model is hindered by unresolved issues around a statistical model fitting to the data. This paper reports our exploratory work in developing a new approach to statistical cusp catastrophe modeling. I... View full abstract»

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  • Using Loglinear Model for Discrimination Discovery and Prevention

    Publication Year: 2016, Page(s):110 - 119
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (242 KB) | HTML iconHTML

    Discrimination discovery and prevention has received intensive attention recently. Discrimination generally refers to an unjustified distinction of individuals based on their membership, or perceived membership, in a certain group, and often occurs when the group is treated less favorably than others. However, existing discrimination discovery and prevention approaches are often limited to examini... View full abstract»

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  • Fraud Detection in Energy Consumption: A Supervised Approach

    Publication Year: 2016, Page(s):120 - 129
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (477 KB) | HTML iconHTML

    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 le... View full abstract»

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  • Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks

    Publication Year: 2016, Page(s):130 - 139
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (605 KB) | HTML iconHTML

    Modern automobiles have been proven vulnerable to hacking by security researchers. By exploiting vulnerabilities in the car's external interfaces, such as wifi, bluetooth, and physical connections, they can access a car's controller area network (CAN) bus. On the CAN bus, commands can be sent to control the car, for example cutting the brakes or stopping the engine. While securing the car's interf... View full abstract»

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  • Anonymizing NYC Taxi Data: Does It Matter?

    Publication Year: 2016, Page(s):140 - 148
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    The widespread use of location-based services has led to an increasing availability of trajectory data from urban environments. These data carry rich information that are useful for improving cities through traffic management and city planning. Yet, it also contains information about individuals which can jeopardize their privacy. In this study, we work with the New York City (NYC) taxi trips data... View full abstract»

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  • Infinite Langevin Mixture Modeling and Feature Selection

    Publication Year: 2016, Page(s):149 - 155
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (206 KB) | HTML iconHTML

    In this paper, we introduce data clustering based on infinite mixture models for spherical patterns. This particular clustering is based on Langevin distribution which has been shown to be effective to model this kind of data. The proposed learning algorithm is tackled using a fully Bayesian approach. In contrast to classical Bayesian approaches, which suppose an unknown finite number of mixture c... View full abstract»

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  • Efficient Identification of Tanimoto Nearest Neighbors

    Publication Year: 2016, Page(s):156 - 165
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (398 KB) | HTML iconHTML

    Tanimoto, or (extended) Jaccard, is an important similarity measure which has seen prominent use in fields such as data mining and chemoinformatics. Many of the existing state-of-the-art methods for market-basket analysis, plagiarism and anomaly detection, compound database search, and ligand-based virtual screening rely heavily on identifying Tanimoto nearest neighbors. Given the rapidly increasi... View full abstract»

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