Multı-Crıterıa Approach To Development Of Machıne Learnıng Methods In Bıoınformatıcs Bio-marker selection is the important part of high dimensional biological data, that is obtain from post-genome, analysis and it aims finding most representative subset of the bio-markers. But selection process is a challenging task due to the high dimensional nature of gene expression data. This should also be independent of sample variations in the dataset. In this paper we present a novel hybrid method that incorporates a multi-objective optimization method, called Pareto Optimal approach (PO) with a multi-criteria decision making method, called Analytical Hierarchy Process (AHP). The method is further supported with different bio-marker selection methods. The multi-criteria approaches proposed in this study were tested on various high-dimensional biological data. The results show that PO method selects the features related to the defined problem in biological data successfully. Furthermore; the results also show that AHP method could be used to prioritize a few selected bio-markers among themselves.
