Multicriteria Models for Learning Ordinal Data: A Literature Review

• Type: Chapter or Part of a Book
• Date: 2013

Authors

Sousa, R (Author) Other; Yevseyeva, I (Author) Other; Da Costa, JFP (Author) FCUP; Cardoso, JS (Author) FEUP

Indexing

ISI Web of Knowledge

Scopus - 11 Citations

Scientific classification

FOS: Natural sciences > Mathematics

Other information

Authenticus ID: P-008-6PT

DOI: 10.1007/978-3-642-29694-9-6

Abstract (EN): Operations Research (OR) and Artificial Intelligence (AI) disciplines have been playing major roles on the design of new intelligent systems. Recently, different contributions from both fields have been made on the models design for problems with multi-criteria. The credit scoring problem is an example of that. In this problem, one evaluates how unlikely a client will default with his payments. Client profiles are evaluated, being their results expressed in terms of an ordinal score scale (Excelent Good Fair Poor). Intelligent systems have then to take in consideration different criteria such as payment history, mortgages, wages among others in order to commit their outcome. To achieve this goal, researchers have been delving models capable to render these multiple criteria encompassed on ordinal data. The literature presents a myriad of different methods either on OR or AI fields for the multi-criteria models. However, a description of ordinal data methods on these two major disciplines and their relations has not been thoroughly conducted yet. It is key for further research to identify the developments made and the present state of the existing methods. It is also important to ascertain current achievements and what the requirements are to attain intelligent systems capable to capture relationships from data. In this chapter one will describe techniques presented for over more than five decades on OR and AI disciplines applied to multi-criteria ordinal problems.

Language: English

Type (Professor's evaluation): Scientific