A new fuzzy-rough feature selection algorithm for mammographic risk analysis

Mammographie risk analysis is a useful means for the early diagnosis of breast cancer. There are many efforts have been devoted to improving the performance of the relevant assessment technologies. This paper presents an invasive weed optimization (IWO) based fuzzy-rough feature selection method for mammographic risk assessment. The advantage of IWO is that the offspring individuals are randomly spread around their parents according to a Gaussian distribution during the evolution process. Such Gaussian distribution is designated with a dynamical standard deviation. Therefore, the optimization algorithm can explore a new solution space aggressively. The diversity of the species can be maintained in the early and middle iterations, and the optimal individuals will be found in the final iteration of feature selection. The mechanism of IWO ensures a global optimal solution for the heuristic search. The performance of IWO is compared against the feature selection methods with ant colony optimization (ACO) and particle swarm optimization (PSO). In the last chapter, the experimental results indicate that the use of IWO entails better performance for the problem of mammographic risk analysis according to both dimensionality reduction and classification accuracy.