A New Approach for Classification of Weighting Methods

Preprocessing steps in MADM are very important and have a critical role in ranking operations. In many ranking algorithms, before running the models, it is necessary to do some modifications. These modifications are for example, Quantification, determination of utility of criteria, dimensionless operations for attributes, weighting methods and so on. Weighting methods try to define an importance of criteria in decision making process. Changing the weight in decision making process has a great influence in ranking results. Sometimes, determination of criteria weights are so difficult with conjoint of errors. There are many methods for this operation, but in literature we can not see an integrated model for classification of these methods. Methods such as entropy, LINMAP, eigenvector, smart, swing and so on, are the main weighting methods in this area. In this paper, our viewpoint for classification is multidimensional. To be or not to be decision making matrix, decision maker role in criteria weighting, number of criterion in weighting operation, and something like that. What is the weight for this criterion in decision making process? To answer, we must try to mine data from two different resources, first considering construction and layout of data in decision making matrix and second from opinion and perception of decision maker. We can see two main problems, first lack off the role of decision maker to finding weights by use decision making matrix and second educe of data from decision maker's opinion without attention to reliability and validity of data is very difficult. This paper tries to introduce a new approach for classification of current weighting methods and make a logical construction for this purpose. At the end, a new combinatorial model for reduce error and produce reliable and valid data for decision making is presented