Designing auction parameters for online industrial auctions is a complex problem due to highly heterogeneous items. Currently, online auctioneers rely heavily on their experts in auction design. The ability of predicting how well an auction will perform prior to the start comes in handy for auctioneers. If an item is expected to be a low-performing item, the auctioneer can take certain actions to influence the auction outcome. For instance, the starting selling price of the item can be modified, or the location where the item is displayed on the website can be changed to attract more attention. In this paper, we take a real-world industrial auction data set and investigate how we can improve upon the expert’s design using insights learned from data. More specifically, we first construct a classification model that predicts the expected performance of auctions. We propose a data driven auction design framework (called DDAD) that combines the expert’s knowledge with the learned prediction model, in order to find the best parameter values, i.e., starting price and display positions of the items, for a given new auction. The prediction model is evaluated, and the new design for several auctions is discussed and validated with the auction experts.@en

A popular method in machine learning for super-vised classification is a decision tree. In this work we propose a new framework to learn fuzzy decision trees using mathematical programming. More specifically, we encode the problem of constructing fuzzy decision trees using a Mixed Integer Linear Programming (MIP) model, which can be solved by any optimization solver. We compare the performance of our method with the performance of off-the-shelf decision tree algorithm CART and Fuzzy Inference Systems (FIS) using benchmark data-sets. Our initial results are promising and show the advantages of using non-crisp boundaries for improving classification accuracy on testing data.@en