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Pairwise versus Pointwise Ranking : A Case Study

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Melnikov V. , Gupta P. , Frick B. , Kaimann D. , Hüllermeier E.

tom Vol. 25

73--83

Object ranking is one of the most relevant problems in the realm of preference learning and ranking. It is mostly tackled by means of two different techniques, often referred to as pairwise and pointwise ranking. In this paper, we present a case study in which we systematically compare two representatives of these techniques, a method based on the reduction of ranking to binary classification and so-called expected rank regression (ERR). Our experiments are meant to complement existing studies in this field, especially previous evaluations of ERR. And indeed, our results are not fully in agreement with previous findings and partly support different conclusions.

Assessment of the Possibility of Imitating Experts’ Aesthetic Judgments about the Impact of Knots on the Beauty of Furniture Fronts Made of Pine Wood

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Gajowniczek K. , Bator M. , Śmietańska K. , Górski J.

Machine Graphics and Vision

2023

tom Vol. 32, No. 2

67--88

Our research aims to reconstruct expert preferences regarding the visual attractiveness of furniture fronts made of pine wood using machine learning algorithms. A numerical experiment was performed using five machine learning algorithms of various paradigms. To find the answer to the question of what determines the expert’s decision, we determined the importance of variables for some machine learning models. For random forest and classification trees, it involves the overall reduction in node impurities resulting from variable splitting, while for neural networks it uses the Garson algorithm. Based on the numerical experiments we can conclude that the best results of expert decision reconstruction are provided by a neural network model. The expert’s decision is better reconstructed for more beautiful images. The decision for nice images is made based on the best 4 or 5 variables, while for ugly images many more features are important. Prettier images and those for which the expert’s decision is better reconstructed have fewer knots.