Overcoming Long Inference Time of Nearest Neighbors Analysis in Regression and Uncertainty Prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00375001" target="_blank" >RIV/68407700:21240/24:00375001 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s42979-024-02670-2" target="_blank" >https://doi.org/10.1007/s42979-024-02670-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42979-024-02670-2" target="_blank" >10.1007/s42979-024-02670-2</a>

Result language

angličtina

Original language name

Overcoming Long Inference Time of Nearest Neighbors Analysis in Regression and Uncertainty Prediction

Original language description

The intuitive approach of comparing like with like, forms the basis of the so-called nearest neighbor analysis, which is central to many machine learning algorithms. Nearest neighbor analysis is easy to interpret, analyze, and reason about. It is widely used in advanced techniques such as uncertainty estimation in regression models, as well as the renowned k-nearest neighbor-based algorithms. Nevertheless, its high inference time complexity, which is dataset size dependent even in the case of its faster approximated version, restricts its applications and can considerably inflate the application cost. In this paper, we address the problem of high inference time complexity. By using gradient-boosted regression trees as a predictor of the labels obtained from nearest neighbor analysis, we demonstrate a significant increase in inference speed, improving by several orders of magnitude. We validate the effectiveness of our approach on a real-world European Car Pricing Dataset with approximately rows for both residual cost and price uncertainty prediction. Moreover, we assess our method’s performance on the most commonly used tabular benchmark datasets to demonstrate its scalability. The link is to github repository where the code is available: https://github.com/koutefra/uncertainty_experiments.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

SN Computer Science

ISSN

2662-995X

e-ISSN

2661-8907

Volume of the periodical

5

Issue of the periodical within the volume

5

Country of publishing house

SG - SINGAPORE

Number of pages

12

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85191305190