Experimental corn hybrids are created in plant breeding programs by crossing two parents, so-called inbred and tester, together. Identification of best parent combinations for crossing is challenging since the total number of possible cross combinations of parents is large and it is impractical to test all possible cross combinations due to limited resources of time and budget. In the 2020 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the historical yield performances of around 4% of total cross combinations of 593 inbreds with 496 testers which were planted in 280 locations between 2016 and 2018 and asked participants to predict the yield performance of cross combinations of inbreds and testers that have not been planted based on the historical yield data collected from crossing other inbreds and testers. In this paper, we present a collaborative filtering method which is an ensemble of matrix factorization method and neural networks to solve this problem. Our computational results suggested that the proposed model significantly outperformed other models such as LASSO, random forest (RF), and neural networks. Presented method and results were produced within the 2020 Syngenta Crop Challenge.

More precisely, given an evaluation of certain evidences, an evidential reasoning scheme generates an evaluation of certain hypotheses. When the evaluation of the evidences Is a binary one, that Is, we either have an evidence or do not have that evidence, the scheme acts as a set function for each hypothesis: a value as an evaluation of the hypothesis Is assigned to each subset of evidences. When the evaluation of hypotheses is also a binary one, the scheme can be represented by a collection of boolean "If-then" rules. Various approaches may be used to mak e this collection more compact. Intermediate concepts, default rules, and other Inventions I Ik e the "choice components" in SEEK2 are among these approaches. The problem becomes more compl lcated when the evaluation of hypotheses uses values from a I inearly ordered set (Integers, real numbers, or I lngulstlc quantifiers) or a partially ordered set (Intervals or property hierarchies). It becomes even more complex when hypotheses are related to each other (Shafer's theory Is an example when hypotheses are subsets of a set), or when the evaluation of evidences are not binary (systems where hypotheses can serve as evidences to other hypothese are examp I es).