Similarity-Based Correlation Functions for Binary Data

Ildar Z. Batyrshin, Ivan Ramirez-Mejia, Ilnur I. Batyrshin, Valery Solovyev

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

The purpose of this study is to survey the correlation and association coefficients introduced previously on the set of binary n-tuples and to determine coefficients satisfying the properties of correlation functions. These functions were recently introduced on the sets with involutive operation as functions generalizing classical correlation coefficients: Pearson’s product-moment correlation, Spearmen’s and Kendall’ rank correlation coefficients, Yule’s Q and Hamann’s association coefficients, etc. It is shown that several, but not all, known correlation and association coefficients defined on the set of binary n-tuples, satisfy the properties of correlation functions. For these association coefficients, there were established similarity measures on the set of binary data that can be used for the generation of these association coefficients. A new parametric family of correlation functions for binary data is proposed. As a particular case, it contains Hamann’s association coefficient.

Idioma originalInglés
Título de la publicación alojadaAdvances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings
EditoresLourdes Martínez-Villaseñor, Hiram Ponce, Oscar Herrera-Alcántara, Félix A. Castro-Espinoza
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas224-233
Número de páginas10
ISBN (versión impresa)9783030608866
DOI
EstadoPublicada - 2020
Evento19th Mexican International Conference on Artificial Intelligence, MICAI 2020 - Mexico City, México
Duración: 12 oct. 202017 oct. 2020

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12469 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia19th Mexican International Conference on Artificial Intelligence, MICAI 2020
País/TerritorioMéxico
CiudadMexico City
Período12/10/2017/10/20

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