Ranking evaluation functions to improve genetic feature selection in content-based image retrieval of mammograms

The ranking problem is a crucial task in the information retrieval systems. In this paper, we take advantage of single valued ranking evaluation functions in order to develop a new method of genetic feature selection tailored to improve the accuracy of content-based image retrieval systems. We propose to boost the feature selection ability of the genetic algorithms (GA) by employing an evaluation criteria (fitness function) that relies on order-based ranking evaluation functions. The evaluation criteria are provided by the GA and has been successfully employed as a measure to evaluate the efficacy of content-based image retrieval process, improving up to 22% the precision of the query answers. Experiments on three medical datasets containing breast cancer diagnosis and breast tissue density analysis showed that fitness functions based on ranking evaluation functions occupy an essential role on the algorithms´ performance, obtaining results significatively better than other fitness function designs. The experiments also showed that the proposed method obtains results superior than feature selection based on the traditional decision-tree C4.5, naive bayes, support vector machine, 1-nearest neighbor and association rule mining.