Time-sensitive classification of behavioral data

Shin Ando, Einoshin Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we address a classification task under a timesensitive setting, in which the amount of observation required to make a prediction is viewed as a practical cost. Such a setting is intrinsic in many systems where the potential reward of the action against the predicted event depends on the response time, e.g., surveillance/warning and diagnostic applications. Meanwhile, predictions are usually less reliable when based on fewer observations, i.e., there exists a trade-off between such temporal cost and the accuracy. We address the task as a classification of subsequences in a time series. The goal is to predict the occurrences of events from subsequent observations and to learn when to commit to the prediction considering the trade-off. We propose an ensemble of classifiers which respectively makes predictions based on subsequences of different lengths. The prediction of the ensemble is given by the earliest confident prediction among the individual classifiers. We propose a cutting-plane algorithm for jointly training an ensemble of linear classifiers considering their temporal dependence. We compare the proposed algorithm against conventional approaches over a collection of behavioral trajectory data.

Original languageEnglish
Title of host publicationProceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013
EditorsJoydeep Ghosh, Zoran Obradovic, Jennifer Dy, Zhi-Hua Zhou, Chandrika Kamath, Srinivasan Parthasarathy
PublisherSiam Society
Pages458-466
Number of pages9
ISBN (Electronic)9781611972627
DOIs
Publication statusPublished - 2013
EventSIAM International Conference on Data Mining, SDM 2013 - Austin, United States
Duration: May 2 2013May 4 2013

Publication series

NameProceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

Other

OtherSIAM International Conference on Data Mining, SDM 2013
CountryUnited States
CityAustin
Period5/2/135/4/13

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software
  • Theoretical Computer Science
  • Information Systems
  • Signal Processing

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