Label ranking aims to learn a mapping from instances to rankings over a finite number of
predefined labels. Random forest is a powerful and one of the most successful general …

With the growing number of insurance purchasers, the sophisticated claim analysis system
has become an imperative must for any insurance firm. Claims Analysis can be utilized to …

One of the important research problems in the context of financial institutions is the
assessment of credit risk and the decision to whether grant or refuse a loan. Recently …

Label Ranking (LR) problems are becoming increasingly important in Machine Learning.
While there has been a significant amount of work on the development of learning …

Preference learning is the branch of machine learning in charge of inducing preference
models from data. In this paper we focus on the task known as label ranking problem, whose …

Information technology is continuously becoming a more central part of society and together
with the increased connectivity and inter-dependency of devices, it is becoming more …

Discretization is an important preprocessing technique, especially in classification problems.
It reduces and simplifies data, accelerates the learning process, and improves learner …

Abstract The problem of Label Ranking is receiving increasing attention from several
research communities. The algorithms that have developed/adapted to treat rankings as the …

In this paper, we investigate two variants of association rules for preference data, Label
Ranking Association Rules and Pairwise Association Rules. Label Ranking Association …

Bank credit rating classifies banks into different levels based on publicly disclosed and
internal information, serving as an important input in financial risk management. However …