research-article

The setwise stream classification problem

Author:

Charu C. AggarwalAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 432 - 441

https://doi.org/10.1145/2623330.2623751

Published: 24 August 2014 Publication History

Abstract

In many applications, classification labels may not be associated with a single instance of records, but may be associated with a data set of records. The class behavior may not be possible to infer effectively from a single record, but may be only be inferred by an aggregate set of records. Therefore, in this problem, the class label is associated with a set of instances both in the training and test data. Therefore, the problem may be understood to be that of classifying a set of data sets. Typically, the classification behavior may only be inferred from the overall patterns of data distribution, and very little information is embedded in any given record for classification purposes. We refer to this problem as the setwise classification problem.

The problem can be extremely challenging in scenarios where the data is received in the form of a stream, and the records within any particular data set may not necessarily be received contiguously. In this paper, we present a first approach for real time and streaming classification of such data. We present experimental results illustrating the effectiveness of the approach.

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Cited By

Strickler EThomas JThomas JBenjamin BShamsuddin R(2023)Exploring a global interpretation mechanism for deep learning networks when predicting sepsisScientific Reports10.1038/s41598-023-30091-313:1Online publication date: 21-Feb-2023
https://doi.org/10.1038/s41598-023-30091-3
Lu ZXu QAl-Rajab MChiazor L(2021)Importance of Research Into Big Data With Machine Learning ApproachMachine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis10.4018/978-1-7998-7316-7.ch008(155-159)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-7316-7.ch008
Challa JGoyal PGiri VMantri DGoyal N(2018)AnySC: Anytime Set-wise Classification of Variable Speed Data Streams2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622567(967-974)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8622567
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Index Terms

The setwise stream classification problem
1. Information systems
  1. Information systems applications

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

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Published: 24 August 2014

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Strickler EThomas JThomas JBenjamin BShamsuddin R(2023)Exploring a global interpretation mechanism for deep learning networks when predicting sepsisScientific Reports10.1038/s41598-023-30091-313:1Online publication date: 21-Feb-2023
https://doi.org/10.1038/s41598-023-30091-3
Lu ZXu QAl-Rajab MChiazor L(2021)Importance of Research Into Big Data With Machine Learning ApproachMachine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis10.4018/978-1-7998-7316-7.ch008(155-159)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-7316-7.ch008
Challa JGoyal PGiri VMantri DGoyal N(2018)AnySC: Anytime Set-wise Classification of Variable Speed Data Streams2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622567(967-974)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8622567
Shamsuddin RSawant APrabhakaran B(2017)Developing a low dimensional patient class profile in accordance to their respiration-induced tumor motionProceedings of the VLDB Endowment10.14778/3137765.313776810:12(1610-1621)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.14778/3137765.3137768
Li YHou DPan AGong ZLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)DeMalCProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132848(1559-1567)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132848
Matsubara YSakurai YPrakash BLi LFaloutsos C(2017)Nonlinear Dynamics of Information Diffusion in Social NetworksACM Transactions on the Web10.1145/305774111:2(1-40)Online publication date: 24-Apr-2017
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Matsubara YSakurai YFaloutsos C(2017)Ecosystem on the WebWorld Wide Web10.1007/s11280-016-0389-x20:3(439-465)Online publication date: 1-May-2017
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Chi PLi SXu CZhang TZhao JLiu YWang YXie Y(2016)PRIMEACM SIGARCH Computer Architecture News10.1145/3007787.300114044:3(27-39)Online publication date: 18-Jun-2016
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Chien ELevi ZWeber O(2016)Bounded distortion parametrization in the space of metricsACM Transactions on Graphics10.1145/2980179.298242635:6(1-16)Online publication date: 5-Dec-2016
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Gao MYiu KNordholm SYe Y(2016)On a New SDP-SOCP Method for Acoustic Source Localization ProblemACM Transactions on Sensor Networks10.1145/296844912:4(1-26)Online publication date: 25-Oct-2016
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