Multiobjective PSO-based rank aggregation: Application in gene ranking from microarray data

Abstract

Rank aggregation refers to the problem of aggregating a number of individual preference rankings of some items to yield a consensus ranking. This problem is usually posed as an optimization problem in which the average distance of the aggregated ranking from the input rank lists is minimized in order to obtain a consensus. However, it may be noted that minimizing the average distance does not always ensure an impartial aggregation. This is because the aggregated ranking may be very similar to one input ranking, whereas at the same time, it may be distant from another. This may reduce the overall average distance while producing a biased aggregated ranking. This possibility motivates us to pose the problem of rank aggregation as a multiobjective optimization problem. Here the objective is not only to minimize the average distance of the aggregated ranking from the input rank lists, but also to minimize the standard deviation of the distance values simultaneously to avoid any bias towards any particular input ranking. In order to minimize both the objective functions simultaneously, we propose a multiobjective particle swarm optimization (PSO) based rank aggregation algorithm in this article. The performance of the proposed technique is demonstrated for some artificial datasets. Moreover, its application in gene ranking from microarray gene expression data is studied and the performance is compared with that of some state-of-art techniques to establish its superiority.