HiPaR: Hierarchical Pattern-Aided Regression

Luis Galárraga; Olivier Pelgrin; Alexandre Termier

doi:10.1007/978-3-030-75762-5_26

HiPaR: Hierarchical Pattern-Aided Regression

Luis Galárraga^*, Olivier Pelgrin, Alexandre Termier

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

1 Citation (Scopus)

Abstract

We introduce HiPaR, a novel pattern-aided regression method for data with both categorical and numerical attributes. HiPaR mines hybrid rules of the form p⇒ y= f(X) where p is the characterization of a data region and f(X) is a linear regression model on a variable of interest y. The novelty of the method lies in the combination of an enumerative approach to explore the space of regions and efficient heuristics that guide the search. Such a strategy provides more flexibility when selecting a small set of jointly accurate and human-readable hybrid rules that explain the entire dataset. As our experiments shows, HiPaR mines fewer rules than existing pattern-based regression methods while still attaining state-of-the-art prediction performance.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings
Editors	Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty
Number of pages	13
Publisher	Springer
Publication date	2021
Pages	320-332
ISBN (Print)	9783030757618
DOIs	https://doi.org/10.1007/978-3-030-75762-5_26
Publication status	Published - 2021
Event	25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 - Virtual, Online Duration: 11 May 2021 → 14 May 2021

Conference

Conference	25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021
City	Virtual, Online
Period	11/05/2021 → 14/05/2021

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12712 LNAI
ISSN	0302-9743

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Linear regression
Rule mining

Access to Document

10.1007/978-3-030-75762-5_26

https://arxiv.org/pdf/2102.12370.pdf

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Galárraga, L., Pelgrin, O., & Termier, A. (2021). HiPaR: Hierarchical Pattern-Aided Regression. In K. Karlapalem, H. Cheng, N. Ramakrishnan, R. K. Agrawal, P. K. Reddy, J. Srivastava, & T. Chakraborty (Eds.), Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings (pp. 320-332). Springer. https://doi.org/10.1007/978-3-030-75762-5_26

Galárraga, Luis ; Pelgrin, Olivier ; Termier, Alexandre. / HiPaR : Hierarchical Pattern-Aided Regression. Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings. editor / Kamal Karlapalem ; Hong Cheng ; Naren Ramakrishnan ; R. K. Agrawal ; P. Krishna Reddy ; Jaideep Srivastava ; Tanmoy Chakraborty. Springer, 2021. pp. 320-332 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12712 LNAI).

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abstract = "We introduce HiPaR, a novel pattern-aided regression method for data with both categorical and numerical attributes. HiPaR mines hybrid rules of the form p⇒ y= f(X) where p is the characterization of a data region and f(X) is a linear regression model on a variable of interest y. The novelty of the method lies in the combination of an enumerative approach to explore the space of regions and efficient heuristics that guide the search. Such a strategy provides more flexibility when selecting a small set of jointly accurate and human-readable hybrid rules that explain the entire dataset. As our experiments shows, HiPaR mines fewer rules than existing pattern-based regression methods while still attaining state-of-the-art prediction performance.",

keywords = "Linear regression, Rule mining",

author = "Luis Gal{\'a}rraga and Olivier Pelgrin and Alexandre Termier",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021 ; Conference date: 11-05-2021 Through 14-05-2021",

year = "2021",

doi = "10.1007/978-3-030-75762-5_26",

language = "English",

isbn = "9783030757618",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "320--332",

editor = "Kamal Karlapalem and Hong Cheng and Naren Ramakrishnan and Agrawal, {R. K.} and Reddy, {P. Krishna} and Jaideep Srivastava and Tanmoy Chakraborty",

booktitle = "Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings",

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Galárraga, L, Pelgrin, O & Termier, A 2021, HiPaR: Hierarchical Pattern-Aided Regression. in K Karlapalem, H Cheng, N Ramakrishnan, RK Agrawal, PK Reddy, J Srivastava & T Chakraborty (eds), Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12712 LNAI, pp. 320-332, 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021, Virtual, Online, 11/05/2021. https://doi.org/10.1007/978-3-030-75762-5_26

HiPaR: Hierarchical Pattern-Aided Regression. / Galárraga, Luis; Pelgrin, Olivier; Termier, Alexandre.
Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings. ed. / Kamal Karlapalem; Hong Cheng; Naren Ramakrishnan; R. K. Agrawal; P. Krishna Reddy; Jaideep Srivastava; Tanmoy Chakraborty. Springer, 2021. p. 320-332 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12712 LNAI).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - HiPaR

T2 - 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2021

AU - Galárraga, Luis

AU - Pelgrin, Olivier

AU - Termier, Alexandre

PY - 2021

Y1 - 2021

N2 - We introduce HiPaR, a novel pattern-aided regression method for data with both categorical and numerical attributes. HiPaR mines hybrid rules of the form p⇒ y= f(X) where p is the characterization of a data region and f(X) is a linear regression model on a variable of interest y. The novelty of the method lies in the combination of an enumerative approach to explore the space of regions and efficient heuristics that guide the search. Such a strategy provides more flexibility when selecting a small set of jointly accurate and human-readable hybrid rules that explain the entire dataset. As our experiments shows, HiPaR mines fewer rules than existing pattern-based regression methods while still attaining state-of-the-art prediction performance.

AB - We introduce HiPaR, a novel pattern-aided regression method for data with both categorical and numerical attributes. HiPaR mines hybrid rules of the form p⇒ y= f(X) where p is the characterization of a data region and f(X) is a linear regression model on a variable of interest y. The novelty of the method lies in the combination of an enumerative approach to explore the space of regions and efficient heuristics that guide the search. Such a strategy provides more flexibility when selecting a small set of jointly accurate and human-readable hybrid rules that explain the entire dataset. As our experiments shows, HiPaR mines fewer rules than existing pattern-based regression methods while still attaining state-of-the-art prediction performance.

KW - Linear regression

KW - Rule mining

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U2 - 10.1007/978-3-030-75762-5_26

DO - 10.1007/978-3-030-75762-5_26

M3 - Article in proceeding

AN - SCOPUS:85111105717

SN - 9783030757618

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 320

EP - 332

BT - Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings

A2 - Karlapalem, Kamal

A2 - Cheng, Hong

A2 - Ramakrishnan, Naren

A2 - Agrawal, R. K.

A2 - Reddy, P. Krishna

A2 - Srivastava, Jaideep

A2 - Chakraborty, Tanmoy

PB - Springer

Y2 - 11 May 2021 through 14 May 2021

ER -

Galárraga L, Pelgrin O, Termier A. HiPaR: Hierarchical Pattern-Aided Regression. In Karlapalem K, Cheng H, Ramakrishnan N, Agrawal RK, Reddy PK, Srivastava J, Chakraborty T, editors, Advances in Knowledge Discovery and Data Mining - 25th Paciﬁc-Asia Conference, PAKDD 2021, Proceedings. Springer. 2021. p. 320-332. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12712 LNAI). doi: 10.1007/978-3-030-75762-5_26

HiPaR: Hierarchical Pattern-Aided Regression

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