An Evolutionary Algorithm for Missing Values Substitution in Classification Tasks

2009 ◽  
pp. 195-202 ◽  
Author(s):  
Jonathan de A. Silva ◽  
Eduardo R. Hruschka
Keyword(s):  
Evolutionary Algorithm ◽  
Missing Values ◽  
Classification Tasks

Bases for Strategy Choice and Strategy Transitions in Binary Classification Tasks

2004 ◽  
Author(s):  
Lyle E. Bourne ◽  
Alice F. Healy ◽  
James A. Kole ◽  
William D. Raymond
Keyword(s):  
Binary Classification ◽  
Strategy Choice ◽  
Classification Tasks

Stacking Schema for Classification Tasks

2012 ◽  
Author(s):  
Eitan Menahem ◽  
Lior Rokach ◽  
Yuval Elovici
Keyword(s):  
Classification Tasks

Missing Data - Better "Not to Have Them", but What If You Do? (Part 1)

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 21-32
Author(s):  
Dirk Temme ◽  
Sarah Jensen
Keyword(s):  
Missing Data ◽  
Statistical Power ◽  
Missing Values ◽  
Graphical Representation ◽  
Marketing Research ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Full Information Maximum Likelihood ◽  
Definition Of ◽  
Traditional Approaches

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Informatica ◽  
2015 ◽  
Vol 26 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Ernestas Filatovas ◽  
Olga Kurasova ◽  
Karthik Sindhya
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Analyzing the Potential of Pre-Trained Embeddings for Audio Classification Tasks

2021 ◽  
Author(s):  
Sascha Grollmisch ◽  
Estefania Cano ◽  
Christian Kehling ◽  
Michael Taenzer
Keyword(s):  
Audio Classification ◽  
Classification Tasks

A parallel multiobjective evolutionary algorithm based on decomposition using MPI and OpenMP

2014 ◽  
Author(s):  
J. W. Liu ◽  
W. Q. Ying ◽  
Y. Wu ◽  
Y. H. Xie ◽  
D. M. Meng ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Multiobjective Evolutionary Algorithm
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