2016 INFORMS Annual Meeting Program

T E C H N I C A L S E S S I O N S

Sunday, 8:00AM - 9:30AM

How to Navigate the Technical Sessions

SA01 101A-MCC Temporal Data Mining and Pattern Discovery Sponsored: Data Mining Sponsored Session Chair: Mustafa Gokce Baydogan, Bogazici University, Bebek, Istanbul, 34342, Turkey, baydoganmustafa@gmail.com 1 - Discovering Distinct Features Using Deep Learning For Arrhythmia Detection Seho Kee, Arizona State University, Tempe, AZ, United States, skee4@asu.edu, Phillip Howard, George Runger Although domain knowledge-based features have been widely adopted in anomaly detection studies, they still suffer from the limitations of the insufficient known features or unavailability in practice. To address these problems, we propose an autoencoder model that is able to discover useful features that identify anomaly patterns in temporal heartbeat data without assuming any prior knowledge. The results show that the discovered features obtained from just a two-dimensional projection layer can effectively distinguish abnormal beats from normal beats without training on pre-labeled data. 2 - Process Control For Time-varying Situations Seoung Bum Kim, Korea University, sbkim1@korea.ac.kr, Seulki Lee In modern manufacturing systems containing the complexity and variability of processes, appropriate control chart techniques that can efficiently handle the nonnormal and nonlinear processes are required. In this talk, I will present some recently developed multivariate control charts to handle both nonnormal and time-varying process situations. 3 - On The Use Of Support Vectors For Time Series Pattern Discovery Mustafa Gokce Baydogan, Bogazici University, Istanbul, Turkey, mustafa.baydogan@boun.edu.tr, Mehmet R Kamber, Erhun Kundakcioglu Similarity search and classification on time series (TS) databases has received great interest over the past decade. The definition of similarity between TS is a major problem in this context. Nearest-neighbor (NN) classifers are widely used for TS classification but these approaches compute the similarity over the whole TS which might be problematic with the long TS and relatively short features of interest. Moreover, these classifiers are not directly interpretable as they do not describe why a TS is assigned to a certain class. This study utilizes margin maximization to discover the regions of the time series that have potentially representative patterns related to the classification task. 4 - Machine Learning For Predicting Heart Failure Readmission Wei Jiang, Research Assistant, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, United States, wjiang1990@gmail.com, Scott R Levin, Lili Barouch, Frederick Korley, Sauleh Ahmad Siddiqui, Diego A. Martinez, Matthew Toerper, Sean Barnes, Eric Hamrock Predicting risk of heart failure (HF) readmission has gained increasing attention, with existing studies mainly using administrative data. We will focus on using clinical data from EMR for predicting HF readmission by doing pattern recognition with time series clinical data. We will then use classification models for predicting the drivers of readmission.

There are four primary resources to help you understand and navigate the Technical Sessions: • This Technical Session listing, which provides the most detailed information. The listing is presented chronologically by day/time, showing each session and the papers/abstracts/authors within each session. • The Author and Session indices provide cross-reference assistance (pages 518-560). Quickest Way to Find Your Own Session Use the Author Index (page 518) — the session code for your presentation will be shown along with the room location. You can also refer to the full session listing for the room location of your session.

The Session Codes

TA01

Room number. Room locations are also indicated in the listing for each session.

Time Block. Matches the time blocks shown in the Program Schedule.

The day of the week

Time Blocks

Sunday - Tuesday 8:00am -9:30am 11:00am – 12:30pm 1:30pm – 3:00pm 4:30pm – 6:00pm Wednesday 8:00am -9:30am 11:00am – 12:30pm 12:45pm -2:15pm 2:45pm – 4:15pm 4:30pm- 6:00pm

Rooms and Locations /Tracks All tracks / technical sessions will be held in the Music City Center and Omni Hotel. Room numbers are shown on the Quick Reference and in the Technical session listing.

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