A folding sum transformation for binary classification

Kangrok Oh; Kar Ann Toh

doi:10.1109/APSIPA.2016.7820861

A folding sum transformation for binary classification

Kangrok Oh, Kar Ann Toh

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we introduce a formulation for a folding sum transformation and then investigate into its impact on binary classification. The proposed folding sum transformation can reduce dimension of data without a training process. The least squares estimation and a full multivariate polynomial expansion are utilized to apply the folding sum transformation for binary classification. Twelve binary data sets from the UCI machine learning repository are utilized in our experimental study. Our results show that the folding sum transformation can either enhance or have comparable accuracy performance at a lower training and testing computational cost comparing with that without using the transformation.

Original language	English
Title of host publication	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9789881476821
DOIs	https://doi.org/10.1109/APSIPA.2016.7820861
Publication status	Published - 2017 Jan 17
Event	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of Duration: 2016 Dec 13 → 2016 Dec 16

Publication series

Name	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

Other

Other	2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
Country/Territory	Korea, Republic of
City	Jeju
Period	16/12/13 → 16/12/16

Bibliographical note

Publisher Copyright:
© 2016 Asia Pacific Signal and Information Processing Association.

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Information Systems
Signal Processing

Access to Document

10.1109/APSIPA.2016.7820861

Cite this

Oh, K., & Toh, K. A. (2017). A folding sum transformation for binary classification. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 [7820861] (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2016.7820861

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abstract = "In this paper, we introduce a formulation for a folding sum transformation and then investigate into its impact on binary classification. The proposed folding sum transformation can reduce dimension of data without a training process. The least squares estimation and a full multivariate polynomial expansion are utilized to apply the folding sum transformation for binary classification. Twelve binary data sets from the UCI machine learning repository are utilized in our experimental study. Our results show that the folding sum transformation can either enhance or have comparable accuracy performance at a lower training and testing computational cost comparing with that without using the transformation.",

author = "Kangrok Oh and Toh, {Kar Ann}",

note = "Publisher Copyright: {\textcopyright} 2016 Asia Pacific Signal and Information Processing Association.; 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 ; Conference date: 13-12-2016 Through 16-12-2016",

year = "2017",

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Oh, K & Toh, KA 2017, A folding sum transformation for binary classification. in 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016., 7820861, 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Institute of Electrical and Electronics Engineers Inc., 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016, Jeju, Korea, Republic of, 16/12/13. https://doi.org/10.1109/APSIPA.2016.7820861

A folding sum transformation for binary classification. / Oh, Kangrok; Toh, Kar Ann.
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7820861 (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - In this paper, we introduce a formulation for a folding sum transformation and then investigate into its impact on binary classification. The proposed folding sum transformation can reduce dimension of data without a training process. The least squares estimation and a full multivariate polynomial expansion are utilized to apply the folding sum transformation for binary classification. Twelve binary data sets from the UCI machine learning repository are utilized in our experimental study. Our results show that the folding sum transformation can either enhance or have comparable accuracy performance at a lower training and testing computational cost comparing with that without using the transformation.

AB - In this paper, we introduce a formulation for a folding sum transformation and then investigate into its impact on binary classification. The proposed folding sum transformation can reduce dimension of data without a training process. The least squares estimation and a full multivariate polynomial expansion are utilized to apply the folding sum transformation for binary classification. Twelve binary data sets from the UCI machine learning repository are utilized in our experimental study. Our results show that the folding sum transformation can either enhance or have comparable accuracy performance at a lower training and testing computational cost comparing with that without using the transformation.

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Oh K, Toh KA. A folding sum transformation for binary classification. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7820861. (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). doi: 10.1109/APSIPA.2016.7820861

A folding sum transformation for binary classification

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