Title :
Learning aggregation weights from 3-tuple comparison sets
Author :
Beliakov, Gleb ; James, Stuart ; Nimmo, Dale
Author_Institution :
Sch. of Inf. Technol., Deakin Univ., Burwood, VIC, Australia
Abstract :
An important task in multiple-criteria decision making is how to learn the weights and parameters of an aggregation function from empirical data. We consider this in the context of quantifying ecological diversity, where such data is to be obtained as a set of pairwise comparisons specifying that one community should be considered more diverse than another. A problem that arises is how to collect a sufficient amount of data for reliable model determination without overloading individuals with the number of comparisons they need to make. After providing an algorithm for determining criteria weights and an overall ranking from such information, we then investigate the improvement in accuracy if ranked 3-tuples are supplied instead of pairs. We found that aggregation models could be determined accurately from significantly fewer 3-tuple comparisons than pairs.
Keywords :
data acquisition; decision making; ecology; environmental science computing; learning (artificial intelligence); operations research; 3-tuple comparison sets; aggregation weight learning; criteria weights; ecological diversity; multiple-criteria decision making; reliable model determination; Accuracy; Biological system modeling; Educational institutions; Generators; Open wireless architecture; Reliability; Vectors;
Conference_Titel :
IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint
Conference_Location :
Edmonton, AB
DOI :
10.1109/IFSA-NAFIPS.2013.6608604