Divergence decision tree classification with Kolmogorov kernel smoothing in high energy physics

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F21%3A00353093" target="_blank" >RIV/68407700:21340/21:00353093 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1088/1742-6596/1730/1/012060" target="_blank" >https://doi.org/10.1088/1742-6596/1730/1/012060</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/1730/1/012060" target="_blank" >10.1088/1742-6596/1730/1/012060</a>

Result language

angličtina

Original language name

Divergence decision tree classification with Kolmogorov kernel smoothing in high energy physics

Original language description

The binary classification of a given dataset is a task of assigning one of the two possible classes to each observation. This can be achieved by many machine learning techniques, e.g. logistic regression, decision trees, neural networks. The supervised divergence decision tree (SDDT) is our own binary classification algorithm in favour of the Rényi divergence, which incorporates multi-dimensional kernel density estimates (KDEs) as the main part of the splitting process in its tree nodes. However, the KDE needs an efficient smoothing in order to obtain quite satisfactory classification results. In this paper, the D-discrepancy method for selecting the bandwidth was applied. It is based on an evaluation of divergences, or distances, between two estimated distributions. The Kolmogorov metric distance on probability space is used and the performance of such a novel technique is compared to standard smoothing techniques. The final goal is to perform a binary classification and achieve the best possible results with respect to the AUC value (area under ROC curve) on a given high energy physics (HEP) dataset, specifically for d+Au heavy ions decay data. This HEP dataset is described and the main structure of the used SDDT is outlined. Final classification results are presented for KDE under Kolmogorov D-method of smoothing in SDDT algorithm.

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

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OECD FORD branch

10103 - Statistics and probability

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Name of the periodical

Journal of Physics Conference Series

ISSN

1742-6588

e-ISSN

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Volume of the periodical

1730

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

6

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-s2.0-85101557186