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RBFACO: A New Feature Selection Algorithm

Medical data sets, including a great amount of disease detection data and a small amount of patient personal data, are always high-dimensional. It is hard to extract potentially useful information from the data sets with low time or memory cost. Feature selection is used to reduce the dimension of data sets by discarding redundant or irrelevant features, and effectively improve the classification performance. Ant colony algorithm, an evolutionary computation algorithm, is a characteristic feature selection algorithm with a good effect on ordinary data sets. However, it cannot work so well in a high-dimensional data set. In this paper, we proposed a Minimum Redundancy Maximum Relevance based ant colony algorithm, which is based on a binary fully-connected graph. In this graph, nodes represent features, and each feature node has two sub-nodes, 0 and 1. Feature nodes are visited randomly by access probability, and the sub-node is chosen by prior knowledge. This method is called RBFACO, and finally it is verified that it can effectively reduce the time cost of feature selection and increase the effect of classification.