Autores
Batyrshin Ildar
Título Similarity-Based Correlation Functions for Binary Data
Tipo Congreso
Sub-tipo CONACYT
Descripción 19th Mexican International Conference on Artificial Intelligence, MICAI 2020
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. © 2020, Springer Nature Switzerland AG.
Observaciones Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) v. 12469 DOI 10.1007/978-3-030-60887-3_20
Lugar Ciudad de México
País Mexico
No. de páginas 224-233
Vol. / Cap. 12469 LNAI
Inicio 2020-10-12
Fin 2020-10-17
ISBN/ISSN 9783030608866