Enhancing Binary Classification by Modeling Uncertain Boundary in Three-Way Decisions

Text classification is a process of classifying documents into predefined categories through different classifiers learned from labelled or unlabelled training samples. Many researchers who work on binary text classification attempt to find a more effective way to separate relevant texts from a large data set. However, current text classifiers cannot unambiguously describe the decision boundary between positive and negative objects because of uncertainties caused by text feature selection and the knowledge learning process. This paper proposes a three-way decision model for dealing with the uncertain boundary to improve the binary text classification performance based on the
rough set
techniques and centroid solution. It aims to understand the uncertain boundary through partitioning the training samples into three regions (the positive, boundary, and negative regions) by two main boundary vectors
$\vec{C_{P}}$
and
$\vec{C_{N}}$
, created from the labeled positive and negative training subsets, respectively, and further resolve the objects in the boundary region by two derived boundary vectors
$\vec{B_{P}}$
and
$\vec{B_{N}}$
, produced according to the structure of the boundary region. It involves an indirect strategy which is composed of two successive steps in the whole classification process: ‘two-way to three-way’ and ‘three-way to two-way’. Four decision rules are proposed from the training process and applied to the incoming documents for more precise classification. A large number of experiments have been conducted based on the standard data sets RCV1 and Reuters-21578. The experimental results show that the usage of boundary vectors is very effective and efficient for dealing with uncertainties of the decision boundary, and the proposed model has significantly improved the performance of binary text classification in terms of
$F_{1}$
measure and
$AUC$
area compared with six other popular baseline models.