On Loss Functions in Label Ranking and Risk Minimization by Pairwise Learning
نویسنده
چکیده
We study the problem of label ranking, a machine learning task that consists of inducing a mapping from instances to rankings over a finite number of labels. Our learning method, referred to as ranking by pairwise comparison (RPC), first induces pairwise order relations (preferences) from suitable training data, using a natural extension of so-called pairwise classification. A ranking is then derived from a set of such relations by means of a ranking procedure. In this paper, we first elaborate on a key advantage of such a decomposition, namely the fact that it allows the learner to adapt to different loss functions without re-training, by using different ranking procedures on the same predicted order relations. In this regard, we distinguish between two types of errors, called, respectively, ranking error and position error. Focusing on the position error, which has received less attention so far, we then propose a ranking procedure called ranking through iterated choice as well as an efficient pairwise implementation thereof. Apart from a theoretical justification of this procedure, we offer empirical evidence in favor of its superior performance as a risk minimizer for the position error. ∗This research has been funded by the German Research Foundation (DFG). It summarizes and extends results presented in [13, 14, 15].
منابع مشابه
Learning Label Preferences: Ranking Error Versus Position Error
We consider the problem of learning a ranking function, that is a mapping from instances to rankings over a finite number of labels. Our learning method, referred to as ranking by pairwise comparison (RPC), first induces pairwise order relations from suitable training data, using a natural extension of so-called pairwise classification. A ranking is then derived from a set of such relations by ...
متن کاملLabel Ranking by Learning Pairwise Preferences Label Ranking by Learning Pairwise Preferences
Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations. In the recent literature, the problem appears in many different guises, which we will first put into a coherent framework. This work then focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to...
متن کاملLabel ranking by learning pairwise preferences
Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations, rather than single values. In the recent literature, the problem appears in many different guises, which we will first put into a coherent framework. This work then focuses on a particular learning scenario called label ranking, where the problem is to learn ...
متن کاملLearning Preference Models from Data: On the Problem of Label Ranking and Its Variants
The term “preference learning” refers to the application of machine learning methods for inducing preference models from empirical data. In the recent literature, corresponding problems appear in various guises. After a brief overview of the field, this work focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to rankings over a ...
متن کاملA Nearest Neighbor Approach to Label Ranking based on Generalized Labelwise Loss Minimization
In this paper, we introduce a new (meta) learning technique for a preference learning problem called label ranking. As opposed to existing meta techniques, which mostly decompose the original problem into pairwise comparisons, our approach relies on a labelwise decomposition. The basic idea is to train one model per class label, namely a model that maps instances to ranks. We propose a concrete...
متن کامل