Strategies for Selection of Positive and Negative Instances in the Hierarchical Classification of Transposable Elements

Abstract

Transposable Elements (TEs) are DNA sequences capable of changing the gene's activity through transposition within the cells of a host. Once TEs insert themselves in other genes, they can change or reduce the activity of certain proteins, which in some cases could unfeasible the survival of such organisms or even provide genetic variability. A variety of methods has been proposed for the identification and classification of TEs, but most of them still involve a lot of manual work or are too class-specific, which restricts its applicability. Besides, the classes involved in such problems are often hierarchically structured, which is ignored by most of these methods. In this scenario, one problem that still needs further investigation is the use of strategies for selecting positive and negative instances during the induction of hierarchical models. Therefore, in this paper we explore four distinct strategies for selecting training instances, making use of several Machine Learning classifiers with different biases which were applied to the Hierarchical Classification of TEs using a local approach. Thus, we recommend the best strategy based on the results experimentally obtained.