The Distance Function Optimization for the Near Neighbors-Based Classifiers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F22%3A00560295" target="_blank" >RIV/67985807:_____/22:00560295 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/70883521:28140/22:63556218

Výsledek na webu

<a href="https://dx.doi.org/10.1145/3434769" target="_blank" >https://dx.doi.org/10.1145/3434769</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3434769" target="_blank" >10.1145/3434769</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Distance Function Optimization for the Near Neighbors-Based Classifiers

Popis výsledku v původním jazyce

Based on the analysis of conditions for a good distance function we found four rules that should be fulfilled. Then, we introduce two new distance functions, a metric and a pseudometric one. We have tested how they fit for distance-based classifiers, especially for the IINC classifier. We rank distance functions according to several criteria and tests. Rankings depend not only on criteria or nature of the statistical test, but also whether it takes into account different difficulties of tasks or whether it considers all tasks as equally difficult. We have found that the new distance functions introduced belong among the four or five best out of 23 distance functions. We have tested them on 24 different tasks, using the mean, the median, the Friedman aligned test, and the Quade test. Our results show that a suitable distance function can improve behavior of distance-based classification rules.

Název v anglickém jazyce

The Distance Function Optimization for the Near Neighbors-Based Classifiers

Popis výsledku anglicky

Based on the analysis of conditions for a good distance function we found four rules that should be fulfilled. Then, we introduce two new distance functions, a metric and a pseudometric one. We have tested how they fit for distance-based classifiers, especially for the IINC classifier. We rank distance functions according to several criteria and tests. Rankings depend not only on criteria or nature of the statistical test, but also whether it takes into account different difficulties of tasks or whether it considers all tasks as equally difficult. We have found that the new distance functions introduced belong among the four or five best out of 23 distance functions. We have tested them on 24 different tasks, using the mean, the median, the Friedman aligned test, and the Quade test. Our results show that a suitable distance function can improve behavior of distance-based classification rules.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/LM2018113" target="_blank" >LM2018113: Výzkumná infrastruktura pro experimenty ve Fermilab</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

ACM Transactions on Knowledge Discovery from Data

ISSN

1556-4681

e-ISSN

1556-472X

Svazek periodika

16

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

101

Kód UT WoS článku

000859375300001

EID výsledku v databázi Scopus

2-s2.0-85140904158