loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Bernardete M. Ribeiro 1 ; Andrew Sung 2 ; Divya Suryakumar 3 and Ram Basnet 4

Affiliations: 1 University of Coimbra, Portugal ; 2 University of Southern Mississippi, United States ; 3 Apple and Inc., United States ; 4 Colorado Mesa University, United States

Keyword(s): Data Mining, Knowledge Discovery, Critical Feature Dimension, Critical Sampling Random Selection.

Related Ontology Subjects/Areas/Topics: Feature Selection and Extraction ; Model Selection ; Pattern Recognition ; Theory and Methods

Abstract: Efficacious data mining methods are critical for knowledge discovery in various applications in the era of big data. Two issues of immediate concern in big data analytic tasks are how to select a critical subset of features and how to select a critical subset of data points for sampling. This position paper presents ongoing research by the authors that suggests: 1. the critical feature dimension problem is theoretically intractable, but simple heuristic methods may well be sufficient for practical purposes; 2. there are big data analytic problems where the success of data mining depends more on the critical feature dimension than the specific features selected, thus a random selection of the features based on the dataset’s critical feature dimension will prove sufficient; and 3. The problem of critical sampling has the same intractable complexity as critical feature dimension, but again simple heuristic methods may well be practicable in most applications.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.147.53.90

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ribeiro, B.; Sung, A.; Suryakumar, D. and Basnet, R. (2015). The Critical Feature Dimension and Critical Sampling Problems. In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-076-5; ISSN 2184-4313, SciTePress, pages 360-366. DOI: 10.5220/0005282403600366

@conference{icpram15,
author={Bernardete M. Ribeiro. and Andrew Sung. and Divya Suryakumar. and Ram Basnet.},
title={The Critical Feature Dimension and Critical Sampling Problems},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2015},
pages={360-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005282403600366},
isbn={978-989-758-076-5},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - The Critical Feature Dimension and Critical Sampling Problems
SN - 978-989-758-076-5
IS - 2184-4313
AU - Ribeiro, B.
AU - Sung, A.
AU - Suryakumar, D.
AU - Basnet, R.
PY - 2015
SP - 360
EP - 366
DO - 10.5220/0005282403600366
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic