Add to ORKGIn computational science literature including, e.g., bioinformatics, computational statistics or machine learning, most published articles are devoted to the development of ‘‘new methods’’, while comparison studies are generally appreciated by readers but surprisingly given poor consideration by many journals. This paper stresses the importance of neutral comparison studies for the objective evaluation of existing methods and the establishment of standards by drawing parallels with clinical research. The goal of the paper is twofold. Firstly, we present a survey of recent computational papers on supervised classification published in seven high-ranking computational science journals. The aim is to provide an up-to-date picture of current sc...
Machine learning (ML) promises to tackle the grand challenges in chemistry and speed up the generati...
In this paper we propose a new criterion for choosing between a pair of classification systems of sc...
In this paper, we develop a novel methodology within the IDCP measuring framework for comparing norm...
In computational science literature including, e.g., bioinformatics, computational statistics or mac...
Abstract. An important component of many data mining projects is finding a good classification algor...
Machine learning has become a powerful tool in various domains, and practitioners are constantly see...
With the advancement of high-throughput technologies, data and computing have become key components ...
Method comparisons are essential to provide recommendations and guidance for applied researchers, wh...
We study whether humans or machine learning (ML) classification models are better at classifying sci...
In this paper, we propose a new criterion for choosing between a pair of classification systems of s...
The field of computational biology has seen dramatic growth over the past few years, both in terms o...
Machine learning is a popular way to find patterns and relationships in high complex datasets. With ...
In science, the relationship between methods and discovery is symbiotic. As we discover more, we are...
The paper compares properties of methods, which are commonly used for the task of classification ana...
The field of computational biology has seen dramatic growth over the past few years, both in terms o...
Machine learning (ML) promises to tackle the grand challenges in chemistry and speed up the generati...
In this paper we propose a new criterion for choosing between a pair of classification systems of sc...
In this paper, we develop a novel methodology within the IDCP measuring framework for comparing norm...
In computational science literature including, e.g., bioinformatics, computational statistics or mac...
Abstract. An important component of many data mining projects is finding a good classification algor...
Machine learning has become a powerful tool in various domains, and practitioners are constantly see...
With the advancement of high-throughput technologies, data and computing have become key components ...
Method comparisons are essential to provide recommendations and guidance for applied researchers, wh...
We study whether humans or machine learning (ML) classification models are better at classifying sci...
In this paper, we propose a new criterion for choosing between a pair of classification systems of s...
The field of computational biology has seen dramatic growth over the past few years, both in terms o...
Machine learning is a popular way to find patterns and relationships in high complex datasets. With ...
In science, the relationship between methods and discovery is symbiotic. As we discover more, we are...
The paper compares properties of methods, which are commonly used for the task of classification ana...
The field of computational biology has seen dramatic growth over the past few years, both in terms o...
Machine learning (ML) promises to tackle the grand challenges in chemistry and speed up the generati...
In this paper we propose a new criterion for choosing between a pair of classification systems of sc...
In this paper, we develop a novel methodology within the IDCP measuring framework for comparing norm...