Enhancing Decision Boundary Setting for Binary Text Classification

Abstract

Text classification is a task of assigning a set of text documents into predefined classes based on the classifier that learns from training samples; labelled or unlabeled. Binary text classifiers provide a way to separate related documents from a large dataset. However, the existing binary text classifiers are not grounded in reality due to the issue of overfitting. They try to find a clear boundary between relevant and irrelevant objects rather than understand the decision boundary. Normally, the decision boundary cannot be described as a clear boundary because of the numerous uncertainties in text documents. This paper attempts to address this issue by proposing an effective model based on sliding window technique (SW) and Support Vector Machine (SVM) to deal with the uncertain boundary and to improve the effectiveness of binary text classification. This model aims to set the decision boundary by dividing the training documents into three distinct regions (positive, boundary, and negative regions) to ensure the certainty of extracted knowledge to describe relevant information. The model then organizes training samples for the learning task to build a multiple SVMs based classifier. The experimental results using the standard dataset Reuters Corpus Volume 1 (RCV1) and TREC topics for text classification, show that the proposed model significantly outperforms six state-of-the-art baseline models in binary text classification.