Hierarchical Classification with Jumping Emerging Patterns

Abstract

Classification is a common task in Machine Learning and Data Mining. Some classification problems are called hierarchical classification problems because they need to take into account a hierarchical taxonomy which establishes an order between involved classes. The protein's function prediction is considered a hierarchical classification problem because their functions are arranged in a hierarchical taxonomy of classes. This paper presents an algorithm for hierarchical classification using the jumping emerging patterns approach. Jumping emerging patterns have been used to flat classification and in this work we explore its adoption in a hierarchical classification scenario. The proposed algorithm was evaluated in eight real datasets, compared against two other recent hierarchical classification algorithms from the literature and also with flat k-nearest neighbor classification algorithm. Preliminary results showed that the proposed approach is an alternative for hierarchical classification, having as main advantages the simplicity and understandability with good accuracy.