Assoc. Prof. Dr. Radovan Kasarda

Committee: International Scientific Committee of Biotechnology and Bioengineering
University: Slovak University of Agriculture in Nitra
Department: Department of Animal Genetics and Breeding Biology
Research Fields: Animal Science, Animal Breeding, Animal Genetics, Quantitative Genetics, Molecular Genetics, Animal Husbandry, Cattle, Horse

Publications

1 Methods for Distinction of Cattle Using Supervised Learning

Authors: Radovan Kasarda, Radoslav Zidek, Veronika Šidlová, Birgit Fuerst-Waltl

Abstract:

Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: Supervised Learning, genetic data, Pinzgau cattle

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1934

Abstracts

1 Methods for Distinction of Cattle Using Supervised Learning

Authors: Radovan Kasarda, Radoslav Zidek, Veronika Šidlová, Birgit Fuerst-Waltl

Abstract:

Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: Machine Learning, Supervised Learning, genetic data, Pinzgau cattle

Procedia PDF Downloads 414