Modelling the Results of the Phadiatop Test Using the Logistic and Ordinal Regression

Kuráňová, Pavlína

doi:10.1007/978-3-319-19147-8_6

Cited by 1 publication

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“…Ordinal regression is a machine learning task which aims to predict variables that take values in an ordered and non-numerical set. It is an interesting research topic with applications in many areas, such as weather forecasting (Guijo-Rubio et al, 2020), medicine (Kuráňová, 2016;Sánchez-Monedero et al, 2018;Beckham & Pal, 2017), psychology (Bürkner & Vuorre, 2019), social media mining (Chakraborty & Church, 2020) or computer vision (Antoniuk et al, 2016;Fu et al, 2018;Liu et al, 2017). This type of problem arises in many situations with human labelers, since they usually prefer to quantify data using categorical values rather than continuous values.…”

Section: Introductionmentioning

confidence: 99%

Ordinal regression with explainable distance metric learning based on ordered sequences

2021

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The purpose of this paper is to introduce a new distance metric learning algorithm for ordinal regression. Ordinal regression addresses the problem of predicting classes for which there is a natural ordering, but the real distances between classes are unknown. Since ordinal regression walks a fine line between standard regression and classification, it is a common pitfall to either apply a regression-like numerical treatment of variables or underrate the ordinal information applying nominal classification techniques. On a different note, distance metric learning is a discipline that has proven to be very useful when improving distance-based algorithms such as the nearest neighbors classifier. In addition, an appropriate distance can enhance the explainability of this model. In our study we propose an ordinal approach to learning a distance, called chain maximizing ordinal metric learning. It is based on the maximization of ordered sequences in local neighborhoods of the data. This approach takes into account all the ordinal information in the data without making use of any of the two extremes of classification or regression, and it is able to adapt to data for which the class separations are not clear. We also show how to extend the algorithm to learn in a non-linear setup using kernel functions. We have tested our algorithm on several ordinal regression problems, showing a high performance under the usual evaluation metrics in this domain. Results are verified through Bayesian non-parametric testing. Finally, we explore the capabilities of our algorithm in terms of explainability using the case-based reasoning approach. We show these capabilities empirically on two different datasets, experiencing significant improvements over the case-based reasoning with the traditional Euclidean nearest neighbors.

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