Multicriteria decision aid in Classification problems

Classification problems refer to the assignment of some alternatives into predefined classes (groups, categories). Such problems often arise in several application fields. For instance, in assessing credit card applications the loan officer must evaluate the characteristics of each applicant and decide whether an application should be accepted or rejected. Similar situations are very common in fields such as finance and economics, production management (fault diagnosis), medicine, customer satisfaction measurement, data base management and retrieval, etc. Addressing a classification problem requires the development of a classification model that aggregates the characteristics of the alternatives to provide recommendations on the assignment of the alternatives to the predefined classes. The significance of classification problems has motivated the development of a plethora of techniques for constructing classification models. Statistical techniques have been dominating the field for many years, but during the last two decades other approaches have become popular mainly from the field of machine learning. The contributions of MCDA are mainly focused on the study of multicriteria classification problems (MCPs). MCPs can be distinguished form traditional classification problems studied within the statistical and machine learning framework in two aspects (Zopounidis and Doumpos, 2002). The first aspect involves the nature of the characteristics describing the alternatives, which are assumed to have the form of decision criteria providing not only a description of the alternatives but also some additional preferential information. The second aspect involves the nature of the predefined classification which is defined in ordinal rather than nominal terms. Classification models developed through statistical and machine learning techniques often fail to address this issues focusing solely on the accuracy of the results obtained from the model. The next two sections describe some important issues on the use and implementation of MCDA classification methods mainly regarding the existing criteria aggregation forms, as well as model development and evaluation issues.