We propose a new adversarial training framework--generative adversarial ranking networks
(GARNet) to learn from user preferences among a list of samples so as to generate data …

Combining multiple ranked lists of items, called voters, into a single consensus list is a
popular problem with significant implications in numerous areas, including Bioinformatics …

Rank aggregation (RA), the technique of combining multiple basic rankings into a
consensus one, plays an important role in social choice, bioinformatics, information retrieval …

Multi-criteria decision making (MCDM) is an effective approach for tackling real-world
decision problems. However, applying different MCDM methods to the same problem may …

Ensemble of label ranking trees (LRTs) are currently the state‐of‐the‐art approaches to the
label ranking problem. Recently, bagging, boosting, and random forest methods have been …

Rank aggregation is a task of combining individual rankings into a consensus, which has
widespread applications in many areas, ranging from social choice to information retrieval …

This paper addresses the problem classification of instances/questions based on the
opinions (classes) provided by anonymous agents. The solution aggregates the agents' …

Label ranking studies the problem of learning a preference model that maps instances to
rankings over a finite set of predefined class labels. The training data used to solve this …

In practice, experts sometimes compare alternatives from different perspectives by rankings
to express their preferences. However, it is common that the comparing information they give …

The prominence of the Chinese language as a United Nations official language has sparked
significant interest, leading to this research on international Chinese education (ICE). This …