Multicriteria Models for Learning Ordinal Data: A Literature Review <s> Multicriteria Decision Analysis <s> The discrimination problem is of major interest in fields such as environmental management, human resources management, production management, finance, marketing, medicine, etc.For decades this problem has been studied from a multivariate statistical point of view. Recently the possibilities of new approaches have been explored, based mainly on mathematical programming. This paper follows the methodological frame work of multicriteria decision aid (MCDA), to propose a new method for mutligroup discrimination based on a hierarchical procedure (Multi-Group Hierarchical Discrimination-M.H. DIS). The performance of the M.H.DIS method is evaluated along with eight real world case studies from the fields of finance and marketing. A Comparison is also performed with other MCDA methods. <s> BIB001 </s> Multicriteria Models for Learning Ordinal Data: A Literature Review <s> Multicriteria Decision Analysis <s> The paper considers major principles of methods based on quantitative measurements. The problem of construction investments is of paramount importance because they determine the potential of construction and the structure of expenses. The cal- culation of investments is an expensive tool of enterprise manage- ment used in planning the investments. It can be perceived as the harmonization and evaluation of the models for investment decision- making. Methods based on a single criterion can hardly be used in solving the problems associated with sophisticated technological or marketing systems. It is a multiple attribute decision method, taking into consideration major efficiency criteria, that enables the effective methods of solving complicated problems. When the quantitative criteria are precisely defined, the developed quantitative multiple criteria decision making (MCDM-1) method can be successfully ap- plied. The present paper is devoted to the analysis of the algorithm and methods of application of the suggested technique. <s> BIB002 </s> Multicriteria Models for Learning Ordinal Data: A Literature Review <s> Multicriteria Decision Analysis <s> Multi-criteria decision analysis (MCDA) methods have become increasingly popular in decision-making for sustainable energy because of the multi-dimensionality of the sustainability goal and the complexity of socio-economic and biophysical systems. This article reviewed the corresponding methods in different stages of multi-criteria decision-making for sustainable energy, i.e., criteria selection, criteria weighting, evaluation, and final aggregation. The criteria of energy supply systems are summarized from technical, economic, environmental and social aspects. The weighting methods of criteria are classified into three categories: subjective weighting, objective weighting and combination weighting methods. Several methods based on weighted sum, priority setting, outranking, fuzzy set methodology and their combinations are employed for energy decision-making. It is observed that the investment cost locates the first place in all evaluation criteria and CO2 emission follows closely because of more focuses on environment protection, equal criteria weights are still the most popular weighting method, analytical hierarchy process is the most popular comprehensive MCDA method, and the aggregation methods are helpful to get the rational result in sustainable energy decision-making. <s> BIB003
Multicriteria decision analysis (MCDA) is an important field within OR. It helped researchers to devise new approaches in order to analyse and interpret human's reasoning. Specifically, when handling several usually conflicting criteria towards an outcome. Such methods are generally composed by five phases depicted in Fig. 6 . This Section will review multicriteria decision methods for ordinal data problems. Alternative formulation and criteria selection is usually defined by a set of experts or DMs BIB002 BIB003 and can depend on the problem in analysis. On the other hand, a given importance (weight) can be defined to each criterion whether through subjective or objective methods. After every criteria being considered, the analysis takes place. In general, MCDA tries to learn about users preferences encompassed in the different criteria considered. One key aspect of such methods is that they do not rely on any statistical assumptions BIB001 . Such highly contrasts with the topic which will be reviewed in Section 4. These two views can mark great differences on both topics, but as one shall see, there are points of connection between these two fields. In doing so, one can identify a trend towards filling the gap between OR and AI on MCDA. Finally, all criteria which were considered are then aggregated in order to define a plausible outcome. It is important to stress that this work is mostly concerned with ordinal data. Hence, not all topics within MCDA can be covered in this Section. The interested reader is referred to  58,   for more information.