Paper Info
Title
Sequential three-way classifier with justifiable granularity.
Abstract
Sequential three-way decisions approach has been demonstrated as an effective methodology of human problem solving with the aid of multiple levels of granularity. Searching an appropriate information granularity for decision or classification is a crucial issue. In this paper, inspired by the principle of justifiable granularity, we investigate a novel classification algorithm, called sequential three-way classifier with justifiable subspace. The major contribution of this study is threefold. First, in training model, with an investigation of the essence of information granularity in rough sets theory, the justifiable attribute subspace is located in an interval with local and global notions. Second, in light of the advantages of attribute reduction technology, the local and global attribute subspaces are determined by core and reduct, respectively. Third, a novel dynamic tri-partition-based predicting strategy is presented with the aid of neighborhood monotonic property. Finally, several experiments are undertaken to verify the effectiveness of the proposed method. Compared with several state-of-the-art classifiers, the proposed algorithm generally exhibits a better classification performance involving fewer attributes.
Year
DOI
Venue
2019
10.1016/j.knosys.2018.08.022
Knowledge-Based Systems
Keywords
Field
DocType
Sequential three-way decisions,Justifiable granularity,Attribute reduction,Classification,Justifiable attribute subspace
Human Problem Solving,Data mining,Monotonic function,Reduct,Subspace topology,Computer science,Rough set,Linear subspace,Artificial intelligence,Granularity,Classifier (linguistics),Machine learning
Journal
Volume
ISSN
Citations 
163
0950-7051
12
PageRank 
References 
Authors
0.44
53
6
Name
Order
Citations
PageRank
Hengrong Ju1752.01
W. Pedrycz2139661005.85
Huaxiong Li377035.51
Weiping Ding427844.96
Xi-bei Yang5121166.36
Xianzhong Zhou643927.01