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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Yuefeng Li
  • Libiao Zhang
  • Yue Xu
  • Yiyu Yao
  • Yutong Wu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number7876817
Pages (from-to)1438-1451
Journal / PublicationIEEE Transactions on Knowledge and Data Engineering
Volume29
Issue number7
Online published13 Mar 2017
Publication statusPublished - Jul 2017

Abstract

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 CP and CN, created from the labeled positive and negative training subsets, respectively, and further resolve the objects in the boundary region by two derived boundary vectors BP and BN, 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 F1 measure and AUC area compared with six other popular baseline models.

Research Area(s)

  • decision rule, rough set, text classification, three-way decision, Uncertain decision boundary

Citation Format(s)

Enhancing Binary Classification by Modeling Uncertain Boundary in Three-Way Decisions. / Li, Yuefeng; Zhang, Libiao; Xu, Yue et al.
In: IEEE Transactions on Knowledge and Data Engineering, Vol. 29, No. 7, 7876817, 07.2017, p. 1438-1451.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review