Data mining

One iteration CHC algorithm for learning Bayesian networks: an effective and efficient algorithm for high dimensional problems

It is well known that learning Bayesian networks from data is an NP-hard problem. For this reason, usually metaheuristics or approximate algorithms have been used to provide a good solution. In particular, the family of hill climbing algorithms has a …

Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood

Learning Bayesian networks is known to be an NP-hard problem and that is the reason why the application of a heuristic search has proven advantageous in many domains. This learning approach is computationally efficient and, even though it does not …

Mining the ESROM: A study of breeding value classification in Manchego sheep by means of attribute selection and construction

Manchego sheep breeding represents an important factor in the economy in the region of Castilla-La Mancha, Spain. For this reason, the selection scheme for Manchego sheep (ESROM) was created to improve milk production in ewes belonging to the …

Initial breeding value prediction on Manchego sheep by using rule-based systems

In this paper we present an application of rule-based expert systems to a farming problem. Concretely the prediction of the breeding value in Manchego ewes is studied for the early stage of their life in which the standard (BLUP) methodology cannot …