scholarly journals A comparison of depth functions in maximal depth classification rules

2017 ◽  
Vol 16 (1) ◽  
pp. 388-405 ◽  
Author(s):  
Olusola Samuel Makinde ◽  
Adeyinka Damilare Adewumi
Keyword(s):  
Classification Rules ◽  
Depth Functions ◽  
Maximal Depth ◽  
Depth Classification

scholarly journals Depth classification based on affine-invariant, weighted and kernel-based spatial depth functions

2021 ◽  
Vol 48 (2) ◽  
Author(s):  
Olusola S. Makinde ◽  
Keyword(s):  
Affine Invariant ◽  
Classification Methods ◽  
Statistical Depth ◽  
Depth Functions ◽  
Maximal Depth ◽  
Depth Classification ◽  
Multivariate Outcomes ◽  
Statistical Depth Functions ◽  
Spatial Depth

Several multivariate depth functions have been proposed in the literature, of which some satisfy all the conditions for statistical depth functions while some do not. Spatial depth is known to be invariant to spherical and shift transformations. In this paper, the possibility of using different versions of spatial depth in classification is considered. The covariance-adjusted, weighted, and kernel-based versions of spatial depth functions are presented to classify multivariate outcomes. We extend the maximal depth classification notions for the covariance-adjusted, weighted, and kernel-based spatial depth versions. The classifiers' performance is considered and compared with some existing classification methods using simulated and real datasets.

scholarly journals Depth-based Classification for Multivariate Data

2017 ◽  
Vol 46 (3-4) ◽  
pp. 117-128 ◽  
Author(s):  
Ondřej Vencálek
Keyword(s):  
Multivariate Data ◽  
Data Depth ◽  
Depth Functions ◽  
Current State ◽  
Short Summary ◽  
Maximal Depth ◽  
Local Depth

Concept of data depth provides one possible approach to the analysis of multivariate data.Among other it can be also used for classification purposes. The present paper is an overview of the research in the field of depth-based classification for multivariate data.It provides a short summary of current state of knowledge in the field of depth-based classification followed by detailed discussion of four main directions in the depth-based classification, namely semiparametric depth-based classifiers, maximal depth classifier, (maximal depth) classifiers which use local depth functions and finally advanced depth-based classifiers.We do not restrict our attention only on proposed classifiers. The paper rather aims to overview the ideas connected with depth-based classification and problems that were discussed in this context.

Learning classification rules from an ion chromatography database using a genetic based classifier system

1997 ◽  
Vol 344 (1-2) ◽  
pp. 1-15 ◽  
Author(s):  
A.H.C. van Kampen ◽  
Z. Ramadan ◽  
M. Mulholland ◽  
D.B. Hibbert ◽  
L.M.C. Buydens
Keyword(s):  
Ion Chromatography ◽  
Classification Rules ◽  
Classifier System
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