Ant Colony Optimization-Based Streaming Feature Selection: An Application to the Medical Image Diagnosis

Irrelevant and redundant features increase the computation and storage requirements, and the extraction of required information becomes challenging. Feature selection enables us to extract the useful information from the given data. Streaming feature selection is an emerging field for the processing of high-dimensional data, where the total number of attributes may be infinite or unknown while the number of data instances is fixed. We propose a hybrid feature selection approach for streaming features using ant colony optimization with symmetric uncertainty (ACO-SU). The proposed approach tests the usefulness of the incoming features and removes the redundant features. The algorithm updates the obtained feature set when a new feature arrives. We evaluate our approach on fourteen datasets from the UCI repository. The results show that our approach achieves better accuracy with a minimal number of features compared with the existing methods.