Add to ORKGThe experimental analysis on the performance of a proposed method is a crucial and nec-essary task to carry out in a research. This paper is focused on the statistical analysis of the results in the community of Genetic Based Machine Learning. Specifically, a non-parametric analysis can be performed by using the average results ob-tained for each data set as sample, which sup-poses in a simpler analysis. We use the well-known non-parametric statistical tests which can be employed and we propose the use of the most powerful statistical techniques to per-form multiple comparisons among more than two algorithms.
Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative t...
This work is builds on the study of the 10 top data mining algorithms identified by the IEEE Interna...
The present paper addresses to the research in the area of regression testing with emphasis on autom...
The experimental analysis on the performance of a proposed method is a crucial and nec-essary task t...
A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their perfo...
<p>Supplementary material of the paper published in International Journal “Information Theories and ...
In a recently published paper in JMLR, Demsar (2006) recommends a set of non-parametric statistical...
This paper proposes a statistical methodology for comparing the performance of evolutionary computat...
Not AvailableGenomic Selection (GS) is the most prevalent method in today’s scenario to access the ...
The mean result of machine learning models is determined by utilizing k-fold cross-validation. The a...
Success rate is a commonly adopted performance criterion for evaluating Evolutionary Algorithms due ...
In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare alg...
Multiparametric assays for risk stratification are widely used in the management of breast cancer, w...
Abstract. Although non-parametric tests have already been proposed for that purpose, sta-tistical si...
This paper grew out of a number of examples arising in data coming from the ENCODE project (Birney e...
Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative t...
This work is builds on the study of the 10 top data mining algorithms identified by the IEEE Interna...
The present paper addresses to the research in the area of regression testing with emphasis on autom...
The experimental analysis on the performance of a proposed method is a crucial and nec-essary task t...
A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their perfo...
<p>Supplementary material of the paper published in International Journal “Information Theories and ...
In a recently published paper in JMLR, Demsar (2006) recommends a set of non-parametric statistical...
This paper proposes a statistical methodology for comparing the performance of evolutionary computat...
Not AvailableGenomic Selection (GS) is the most prevalent method in today’s scenario to access the ...
The mean result of machine learning models is determined by utilizing k-fold cross-validation. The a...
Success rate is a commonly adopted performance criterion for evaluating Evolutionary Algorithms due ...
In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare alg...
Multiparametric assays for risk stratification are widely used in the management of breast cancer, w...
Abstract. Although non-parametric tests have already been proposed for that purpose, sta-tistical si...
This paper grew out of a number of examples arising in data coming from the ENCODE project (Birney e...
Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative t...
This work is builds on the study of the 10 top data mining algorithms identified by the IEEE Interna...
The present paper addresses to the research in the area of regression testing with emphasis on autom...