Article

Online feature selection for pixel classification

Authors:

Damian Eads, and

James TheilerAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

Pages 249 - 256

https://doi.org/10.1145/1102351.1102383

Published: 07 August 2005 Publication History

Abstract

Online feature selection (OFS) provides an efficient way to sort through a large space of features, particularly in a scenario where the feature space is large and features take a significant amount of memory to store. Image processing operators, and especially combinations of image processing operators, provide a rich space of potential features for use in machine learning for image processing tasks but they are expensive to generate and store. In this paper we apply OFS to the problem of edge detection in grayscale imagery. We use a standard data set and compare our results to those obtained with traditional edge detectors, as well as with results obtained more recently using "statistical edge detection." We compare several different OFS approaches, including hill climbing, best first search, and grafting.

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Cited By

Villa-Blanco CBielza CLarrañaga P(2023)Feature subset selection for data and feature streams: a reviewArtificial Intelligence Review10.1007/s10462-023-10546-956:S1(1011-1062)Online publication date: 13-Jul-2023
https://doi.org/10.1007/s10462-023-10546-9
Yang JFan GShen AWang A(2021)Causal structure learning of nonlinear additive noise model based on streaming feature2021 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW53433.2021.00066(490-499)Online publication date: Dec-2021
https://doi.org/10.1109/ICDMW53433.2021.00066
Chen JPatra JPradel MXiong YZhang HHao DZhang L(2020)A Survey of Compiler TestingACM Computing Surveys10.1145/336356253:1(1-36)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3363562
Show More Cited By

Online feature selection for pixel classification
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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Published In

cover image ACM Other conferences

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2005

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Cited By

Villa-Blanco CBielza CLarrañaga P(2023)Feature subset selection for data and feature streams: a reviewArtificial Intelligence Review10.1007/s10462-023-10546-956:S1(1011-1062)Online publication date: 13-Jul-2023
https://doi.org/10.1007/s10462-023-10546-9
Yang JFan GShen AWang A(2021)Causal structure learning of nonlinear additive noise model based on streaming feature2021 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW53433.2021.00066(490-499)Online publication date: Dec-2021
https://doi.org/10.1109/ICDMW53433.2021.00066
Chen JPatra JPradel MXiong YZhang HHao DZhang L(2020)A Survey of Compiler TestingACM Computing Surveys10.1145/336356253:1(1-36)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3363562
Mauceri SSweeney JMcDermott J(2020)One-Class Subject Authentication Using Feature Extraction by Grammatical Evolution on Accelerometer DataHeuristics for Optimization and Learning10.1007/978-3-030-58930-1_26(393-407)Online publication date: 16-Dec-2020
https://doi.org/10.1007/978-3-030-58930-1_26
Hatami NGavet YDebayle J(2019)Bag of recurrence patterns representation for time-series classificationPattern Analysis & Applications10.1007/s10044-018-0703-622:3(877-887)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10044-018-0703-6
Javidi MEskandari S(2019)Online streaming feature selectionPattern Analysis & Applications10.1007/s10044-018-0690-722:3(949-963)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s10044-018-0690-7
Bolón-Canedo VAlonso-Betanzos ABolón-Canedo VAlonso-Betanzos A(2018)Emerging ChallengesRecent Advances in Ensembles for Feature Selection10.1007/978-3-319-90080-3_10(173-205)Online publication date: 1-May-2018
https://doi.org/10.1007/978-3-319-90080-3_10
Filho RPorter B(2017)Defining Emergent Software Using Continuous Self-Assembly, Perception, and LearningACM Transactions on Autonomous and Adaptive Systems10.1145/309269112:3(1-25)Online publication date: 20-Sep-2017
https://dl.acm.org/doi/10.1145/3092691
Wu YHoi SMei TYu N(2017)Large-Scale Online Feature Selection for Ultra-High Dimensional Sparse DataACM Transactions on Knowledge Discovery from Data10.1145/307064611:4(1-22)Online publication date: 29-Jun-2017
https://dl.acm.org/doi/10.1145/3070646
Sasikala SDevi D(2017)A review of traditional and swarm search based feature selection algorithms for handling data stream classification2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS)10.1109/SSPS.2017.8071650(514-520)Online publication date: May-2017
https://doi.org/10.1109/SSPS.2017.8071650
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