Analysing the Influence of Two Similarity Metrics on the Ant Colony Optimisation Based Fuzzy-Rough Feature Selection Algorithm

As the amount of the data increases in volume, veracity, and value, and thus the number of the features rises, so the standard algorithms will find it difficult to process the data without help of huge computer power. However, the feature selection methodology offers help for this issue. In its core, the feature selection tries to find the most predictive input features for the output (target) feature. Feature selection combined with a hybrid variant of rough sets, fuzzy-rough sets provides fuzzy-rough feature selection that could offer better results in this task. To help fuzzy-rough methods to find optimal subsets, attempts have been made using feature selection mechanism based on Ant Colony optimisation (ACO). This approach is applied to the problem of finding optimal subset of features in fuzzy-rough data reduction process by using different similarity metrics. In this paper, we investigate the influence of two fuzzy similarity metrics on the performance of the feature selection ACO strategy. The investigation is made by using several datasets. We experimentally compare several classical classification algorithms by using the AUC-ROC evaluation measure. Additionally, we show the benefits of making feature reduction.