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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Journal of Physics Conference Series

ISSN

1742-6588

e-ISSN

—

Svazek periodika

1730

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

6

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85101557186