Mating decision support system using computer neural network model in Kenyan Holstein-Friesian dairy cattle

Citation:
Njubi DM, Wakhungu J, Badamana MS. "Mating decision support system using computer neural network model in Kenyan Holstein-Friesian dairy cattle.". 2009.

Abstract:

Knowledge discovery in databases (KDD) should provide not only accurate predictions but also comprehensible rules. In this paper, we demonstrate that the machine learning approach of rule extraction from a computer trained neural network system can successfully be applied to milk production analyses in dairy cattle. Such extracted knowledge should be useful in interpretation and understanding how the neural network (NN) model makes its decision. Data consisting of 6095 lactation records made by cows from 76 officially milk recorded Holstein Friesian herds in the period 1988-2005 were used to extract rules using neural network. Two different methods of attribute categorization; auto-class and the domain expert were used. For automated knowledge acquisition, rule induction used Weka software while SAS was used in domain expert. The neural nets were first trained to identify outputs for different inputs. The trained networks were then used for rule extraction. The study showed that the decision trees generated from the trained network had higher accuracy than decision trees created directly from the data. The study also indicated a need for a process to determine important inputs before using a neural net and showed that reduced input sets may produce more accurate neural nets and more compact decision trees. The “black-box” nature of neural networks was explained by extracting rules with both the domain expert and autoclass for both the continuous and the discrete valued inputs with rule sets performing better on the ‘low’ and ‘high’ levels. It follows from these analyses that performance at the two extremes was more important than average performance. It implied that the end user was particularly concerned with identifying mating with good potential and avoid mating with poor potential animals. The decision tree showed that when the herd performance was low then the foremost limiting factor was the dam performance whereas for medium and high herd performance sire level performance was the limiting factor. Through sensitivity analysis the most important and sensible factors with respect to productivity were sire breeding value and herd performance. It was, therefore, concluded that neural network rule extraction and decision tables were powerful management tools that allow the building of advanced and user-friendly decision-support systems for mating strategy designs and their evaluation.

UoN Websites Search