title: A novel algorithm for imbalance data classification based on neighborhood hypergraph.
abstract: The classification problem for imbalance data is paid more attention to. So far, many significant methods are proposed and applied to many fields. But more efficient methods are needed still. Hypergraph may not be powerful enough to deal with the data in boundary region, although it is an efficient tool to knowledge discovery. In this paper, the neighborhood hypergraph is presented, combining rough set theory and hypergraph. After that, a novel classification algorithm for imbalance data based on neighborhood hypergraph is developed, which is composed of three steps: initialization of hyperedge, classification of training data set, and substitution of hyperedge. After conducting an experiment of 10-fold cross validation on 18 data sets, the proposed algorithm has higher average accuracy than others.
authors.affiliation: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
authors.forename: Feng
authors.identifier: 0000-0002-1434-2429
authors.initials: F
authors.lastname: Hu
authors.affiliation: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
authors.forename: Xiao
authors.identifier: 0000-0002-3399-5601
authors.initials: X
authors.lastname: Liu
authors.affiliation: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
authors.forename: Jin
authors.identifier: 0000-0002-4610-4767
authors.initials: J
authors.lastname: Dai
authors.affiliation: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Yu
mesh_terms: D055641:Mathematical Concepts