Data Fusion Performance Prophecy: A Random Forest Revelation

Zhongmin Zhang; Shengli Wu

Data Fusion Performance Prophecy: A Random Forest Revelation

Zhongmin Zhang
, Shengli Wu

Jiangsu University

Research output: Contribution to conference › Paper › peer-review

Abstract

Data fusion synthesizes results from diverse sources, but the performance impact remains mysterious. This research reveals the inner workings of fusion through machine prophecy. Constructing a random forest model using TREC dataset benchmarks, we accurately predicted the performance of two fusion
algorithms. The model achieved near perfect R2 scores above 0.9 by exploiting meaningful statistical features. Compared to linear regression, the tree-based
ensemble provides superior insight. The importance of newly identified drivers,
like P@1000 metrics, is quantified. With this prescient view, researchers can
refine fusion techniques to offer better search. By uncovering the secrets of fusion
success, machine learning guides the path to retrieval excellence.

Original language	English
Pages	192-200
Number of pages	9
Publication status	Published (in print/issue) - 1 Dec 2023
Event	25th International Conference on Information Integration and Web Intelligence - Denpasar, Bali, Indonesia Duration: 4 Dec 2023 → 6 Dec 2023 https://www.iiwas.org/conferences/iiwas2023/

Conference

Conference	25th International Conference on Information Integration and Web Intelligence
Abbreviated title	iiWAS 2023
Country/Territory	Indonesia
City	Bali
Period	4/12/23 → 6/12/23
Internet address	https://www.iiwas.org/conferences/iiwas2023/

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

data fusion
performance prediction
random forest
information retrieval

Access to Document

https://link.springer.com/chapter/10.1007/978-3-031-48316-5_20Licence: Other

Cite this

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title = "Data Fusion Performance Prophecy: A Random Forest Revelation",

abstract = "Data fusion synthesizes results from diverse sources, but the performance impact remains mysterious. This research reveals the inner workings of fusion through machine prophecy. Constructing a random forest model using TREC dataset benchmarks, we accurately predicted the performance of two fusion algorithms. The model achieved near perfect R2 scores above 0.9 by exploiting meaningful statistical features. Compared to linear regression, the tree-based ensemble provides superior insight. The importance of newly identified drivers, like P@1000 metrics, is quantified. With this prescient view, researchers can refine fusion techniques to offer better search. By uncovering the secrets of fusion success, machine learning guides the path to retrieval excellence.",

keywords = "data fusion, performance prediction, random forest, information retrieval",

author = "Zhongmin Zhang and Shengli Wu",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Information Integration and Web Intelligence, iiWAS 2023 ; Conference date: 04-12-2023 Through 06-12-2023",

year = "2023",

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day = "1",

language = "English",

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TY - CONF

T1 - Data Fusion Performance Prophecy: A Random Forest Revelation

AU - Zhang, Zhongmin

AU - Wu, Shengli

PY - 2023/12/1

Y1 - 2023/12/1

N2 - Data fusion synthesizes results from diverse sources, but the performance impact remains mysterious. This research reveals the inner workings of fusion through machine prophecy. Constructing a random forest model using TREC dataset benchmarks, we accurately predicted the performance of two fusion algorithms. The model achieved near perfect R2 scores above 0.9 by exploiting meaningful statistical features. Compared to linear regression, the tree-based ensemble provides superior insight. The importance of newly identified drivers, like P@1000 metrics, is quantified. With this prescient view, researchers can refine fusion techniques to offer better search. By uncovering the secrets of fusion success, machine learning guides the path to retrieval excellence.

AB - Data fusion synthesizes results from diverse sources, but the performance impact remains mysterious. This research reveals the inner workings of fusion through machine prophecy. Constructing a random forest model using TREC dataset benchmarks, we accurately predicted the performance of two fusion algorithms. The model achieved near perfect R2 scores above 0.9 by exploiting meaningful statistical features. Compared to linear regression, the tree-based ensemble provides superior insight. The importance of newly identified drivers, like P@1000 metrics, is quantified. With this prescient view, researchers can refine fusion techniques to offer better search. By uncovering the secrets of fusion success, machine learning guides the path to retrieval excellence.

KW - data fusion

KW - performance prediction

KW - random forest

KW - information retrieval

UR - https://www.scopus.com/pages/publications/85178665575

M3 - Paper

SP - 192

EP - 200

T2 - 25th International Conference on Information Integration and Web Intelligence

Y2 - 4 December 2023 through 6 December 2023

ER -

Data Fusion Performance Prophecy: A Random Forest Revelation

Abstract

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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Cite this