You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Multicriteria Models for Learning Ordinal Data: A Literature Review 
Chapter Title:  Definition 1 (Classification on Ordinal Data Problems[16, 18, 91, 93, 146]). 
1. Abstract of Cited Paper (BIB001):  The mathematical representation of human preferences has been a subject of study for researchers in different fields. In multi-criteria decision making (MCDM) and fuzzy modeling, preference models are typically constructed by interacting with the human decision maker (DM). However, it is known that a DM often has difficulties to specify precise values for certain parameters of the model. He/she instead feels more comfortable to give holistic judgements for some of the alternatives. Inference and elicitation procedures then assist the DM to find a satisfactory model and to assess unjudged alternatives. In a related but more statistical way, machine learning algorithms can also infer preference models with similar setups and purposes, but here less interaction with the DM is required/allowed. In this article we discuss the main differences between both types of inference and, in particular, we present a hybrid approach that combines the best of both worlds. This approach consists of a very general kernel-based framework for constructing and inferring preference models. Additive models, for which interpretability is preserved, and utility models can be considered as special cases. Besides generality, important benefits of this approach are its robustness to noise and good scalability. We show in detail how this framework can be utilized to aggregate single-criterion outranking relations, resulting in a flexible class of preference models for which domain knowledge can be specified by a DM.

Literature Review:
Classifying on ordinal data problems consists on finding the best mapping f : Pattern x x x is also referred as instance, example or alternative. Moreover, x x x can be represented in a vector fashion where each entry is identified as a feature, attribute or criterion, i.e., x x x = {x 1 ,... ,x d }. A dataset is a tuple consisted of N patterns and its target classes (or outcomes), and , the order relation on the set of classes. Literature usually differentiates attribute and criterion BIB001 . Consequently, the problem in analysis can be substantially different. In an ordinal data problem as the credit scoring, an alternative (to which client a loan should be granted) is characterised by several criteria, each one representing a level of importance to the decision maker (DM) (the bank). Here, criteria is used instead of attribute being the former more adequate for the ordinal problem BIB001 BIB002 . The usage of the term ranking is also common in the MC field. However, such term is usually mentioned to other subjects aside classification.