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Learning decision trees for the partial label ranking problem
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DOI：10.1002/int.22325.png)
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Abstract
En 中文
The Label Ranking (LR) problem is a well-known nonstandard supervised classification problem, the goal of which is to learn preference classifiers from data, mapping instances to rankings of the labels of the class variable. In the literature, the particular setting where the output of the LR problem is a complete ranking without ties (a.k.a. permutation) has been profusely studied, and many algorithms have been designed to solve these particular instances based on the use of specific probability distributions and aggregation methods for permutations. However, also partial orders (a.k.a. bucket orders) can be considered as output in LR problems (i.e., some labels of the class variable may be tied), but the algorithms available do not tackle this kind of ranking. We refer to this particular case of LR as the Partial Label Ranking (PLR) problem. Thus, motivated by the lack of current methods to deal with the PLR problem, we design machine learning algorithms based on instance-based and decision tree approaches to tackle the PLR problem. We evaluate our proposals on a benchmark of 15 data sets obtained by transforming multiclass instances, and analyze their performance by carrying out a standard machine learning statistical analysis procedure.
Keywords:
decision trees
instance‐ based learning
label ranking problem
partial label ranking problem
supervised classification
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