2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

19-21 Oct. 2017

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  • [Front cover]

    Publication Year: 2017, Page(s): c1
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  • [Title page i]

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  • [Copyright notice]

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  • Table of contents

    Publication Year: 2017, Page(s):v - xii
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  • Message from General Chairs

    Publication Year: 2017, Page(s):xiii - xiv
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  • Message from Program Chairs

    Publication Year: 2017, Page(s):xv - xvi
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  • Conference Organization

    Publication Year: 2017, Page(s):xvii - xviii
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  • Program Committee

    Publication Year: 2017, Page(s):xix - xxiv
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  • Reviewers

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  • The k-Nearest Representatives Classifier: A Distance-Based Classifier with Strong Generalization Bounds

    Publication Year: 2017, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (416 KB) | HTML iconHTML

    We define the k-Nearest Representatives (k-NR) classifier, a distance-based classifier similar to the k-nearest neighbors classifier with comparable accuracy in practice, and stronger generalization bounds. Uniform convergence is shown through Rademacher complexity, and generalizability is controlled through regularization. Finite-sample risk bound are also given. Compared to the k-NN, the k-NR re... View full abstract»

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  • Cyclic Classifier Chain for Cost-Sensitive Multilabel Classification

    Publication Year: 2017, Page(s):11 - 20
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (220 KB) | HTML iconHTML

    We propose a novel method, Cyclic Classifier Chain (CCC), for multilabel classification. CCC extends the classic Classifier Chain (CC) method by cyclically training multiple chains of labels. Three benefits immediately follow the cyclic design. First, CCC resolves the critical issue of label ordering in CC, and therefore reaches more stable performance. Second, CCC matches the task of cost-sensiti... View full abstract»

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  • Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization

    Publication Year: 2017, Page(s):21 - 29
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (338 KB) | HTML iconHTML

    Recently, neural embeddings of documents have shown success in various language processing tasks. These lowdimensional and dense feature vectors of text documents capture semantic similarities better than traditional methods. However, the underlying optimization problem is non-convex and usually solved using stochastic gradient descent. Hence solutions are most-likely sub-optimal and not reproduci... View full abstract»

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  • Learning Through Utility Optimization in Regression Tasks

    Publication Year: 2017, Page(s):30 - 39
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (11582 KB) | HTML iconHTML

    Accounting for misclassification costs is important in many practical applications of machine learning, and cost-sensitive techniques for classification have been studied extensively. Utility-based learning provides a generalization of purely cost-based approaches that considers both costs and benefits, enabling application to domains with complex cost-benefit settings. However, there is little wo... View full abstract»

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  • Animal Recognition and Identification with Deep Convolutional Neural Networks for Automated Wildlife Monitoring

    Publication Year: 2017, Page(s):40 - 49
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (835 KB) | HTML iconHTML

    Efficient and reliable monitoring of wild animals in their natural habitats is essential to inform conservation and management decisions. Automatic covert cameras or "camera traps" are being an increasingly popular tool for wildlife monitoring due to their effectiveness and reliability in collecting data of wildlife unobtrusively, continuously and in large volume. However, processing... View full abstract»

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  • Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment

    Publication Year: 2017, Page(s):50 - 59
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1436 KB) | HTML iconHTML

    We propose Nazr-CNN1, a deep learning pipeline for object detection and fine-grained classification in images acquired from Unmanned Aerial Vehicles (UAVs) for damage assessment and monitoring. Nazr-CNN consists of two components. The function of the first component is to localize objects (e.g. houses or infrastructure) in an image by carrying out a pixel-level classification. In the second compon... View full abstract»

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  • Website Navigation Behavior Analysis for Bot Detection

    Publication Year: 2017, Page(s):60 - 68
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (350 KB) | HTML iconHTML

    Detecting bots is an important goal for most website admins. In this paper, we propose a novel machine learning bot detection approach based on local website navigation behavior. While machine learning has been used before for bot detection, most existing approaches rely on general hypotheses based on statistical analysis over multiple websites and are thus easy to counter. In our work, we build a... View full abstract»

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  • Inform Product Change through Experimentation with Data-Driven Behavioral Segmentation

    Publication Year: 2017, Page(s):69 - 78
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (603 KB) | HTML iconHTML

    Online controlled experimentation is widely adopted for evaluating new features in the rapid development cycle for web products and mobile applications. Measurement on overall experiment sample is a common practice to quantify the overall treatment effect. In order to understand why the treatment effect occurs in a certain way, segmentation becomes a valuable approach to a finer analysis of experi... View full abstract»

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  • Scalable RFM-enriched Representation Learning for Churn Prediction

    Publication Year: 2017, Page(s):79 - 88
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (286 KB) | HTML iconHTML

    Most of the recent studies on churn prediction in telco utilize social networks built on top of the call (and/or SMS) graphs to derive informative features. However, extracting features from large graphs, especially structural features, is an intricate process both from a methodological and computational perspective. Due to the former, feature extraction in the current literature has mainly been a... View full abstract»

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  • A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks

    Publication Year: 2017, Page(s):89 - 98
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (852 KB) | HTML iconHTML

    In real-world social networks, there is an increasing interest in tracking the evolution of groups of users and detecting the various changes they are liable to undergo. Several approaches have been proposed for this. In studying these approaches, we observed that most of them use a two-stage process. In the first stage, they run an algorithm to identify groups of users at each timestamp. In the s... View full abstract»

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  • The Initialization and Parameter Setting Problem in Tensor Decomposition-Based Link Prediction

    Publication Year: 2017, Page(s):99 - 108
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (262 KB) | HTML iconHTML

    Link prediction is the task of social network analysis whose goal is to predict the links that will appear in the network in future instants. Among the link predictors exploiting the time evolution of the networks, we can find the tensor decomposition-based methods. A major limitation of these methods is the lack of appropriate approaches for estimating their parameters and initialization. In this... View full abstract»

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  • Combining Instance and Feature Neighbors for Efficient Multi-label Classification

    Publication Year: 2017, Page(s):109 - 118
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (205 KB) | HTML iconHTML

    Multi-label classification problems occur naturally in different domains. For example, within text categorization the goal is to predict a set of topics for a document, and within image scene classification the goal is to assign labels to different objects in an image. In this work we propose a combination of two variations of k nearest neighborhoods (kNN) where the first neighborhood is computed ... View full abstract»

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  • Expert Estimates for Feature Relevance are Imperfect

    Publication Year: 2017, Page(s):119 - 128
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (577 KB) | HTML iconHTML

    An early step in the knowledge discovery process is deciding on what data to look at when trying to predict a given target variable. Most of KDD so far is focused on the workflow after data has been obtained, or settings where data is readily available and easily integrable for model induction. However, in practice, this is rarely the case, and many times data requires cleaning and transformation ... View full abstract»

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  • Multi-label Learning with Label-Specific Features via Clustering Ensemble

    Publication Year: 2017, Page(s):129 - 136
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (411 KB) | HTML iconHTML

    Multi-label learning deals with objects with rich semantics where each example is associated with multiple class labels simultaneously. Intuitively, each class label is supposed to possess specific characteristics of its own. Therefore, exploiting label-specific features serves as one of the promising techniques to learn from multi-label examples. Specifically, the LIFT approach generates the labe... View full abstract»

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  • Customizing Travel Packages with Interactive Composite Items

    Publication Year: 2017, Page(s):137 - 145
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (917 KB) | HTML iconHTML

    We examine the applicability of Composite Items (CIs) for generating customized travel packages consisting of Points of Interest (POIs) in a given city. CIs have been shown to serve complex information needs such as selecting books for a reading club, identifying a set of products for a promotion, or planning a city tour. In the travel domain, a synthesized view of travel options in a city can be ... View full abstract»

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