Dynamic Instance-Wise Joint Feature Selection and Classification

Yasitha Warahena Liyanage, Daphney-Stavrou Zois, Charalampos Chelmis

Abstract: In this article, a dynamic instance-wise joint feature selection and classification framework during testing is presented. Specifically, the proposed framework sequentially selects features one at a time for each data instance, given previously selected features, and stops this process to classify the instance once it determines that including additional features will not improve the final classification decision. In contrast to most of the existing work that utilizes a set of features, common for all data instances, the proposed framework utilizes different features to classify each data instance. An optimization problem is defined for each data instance in terms of the number of selected features and the associated classification accuracy. The optimum solution is derived, and its structure is analyzed. Based on the optimum solution and its properties, two new algorithms are designed. The expected number of features needed to achieve a given classification accuracy is also analytically derived. Finally, the performance of the proposed algorithms is illustrated on 11 public datasets, thus demonstrating their effectiveness and scalability across a broad range of application domains.