Boundary-Eliminated Pseudoinverse Linear Discriminant for Imbalanced Problems

Existing learning models for classification of imbalanced data sets can be grouped as either boundary-based or nonboundary-based depending on whether a decision hyperplane is used in the learning process. The focus of this paper is a new approach that leverages the advantage of both approaches. Spec...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2018-06, Vol.29 (6), p.2581-2594
Main Authors: Zhu, Yujin, Wang, Zhe, Zha, Hongyuan, Gao, Daqi
Format: Article
Language:English
Subjects:
Adaptation models
Classification
Classifiers
Datasets
Degradation
Heuristic method
Hyperplanes
imbalanced data
k-nearest neighbor (<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k NN) rule
Learning systems
pattern classification
Predictive models
pseudoinverse linear discriminant (PILD)
Standard data
State of the art
Support vector machines
Testing
Training
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Summary:Existing learning models for classification of imbalanced data sets can be grouped as either boundary-based or nonboundary-based depending on whether a decision hyperplane is used in the learning process. The focus of this paper is a new approach that leverages the advantage of both approaches. Specifically, our new model partitions the input space into three parts by creating two additional boundaries in the training process, and then makes the final decision based on a heuristic measurement between the test sample and a subset of selected training samples. Since the original hyperplane used by the underlying original classifier will be eliminated, the proposed model is named the boundary-eliminated (BE) model. Additionally, the pseudoinverse linear discriminant (PILD) is adopted for the BE model so as to obtain a novel classifier abbreviated as BEPILD. Experiments validate both the effectiveness and the efficiency of BEPILD, compared with 13 state-of-the-art classification methods, based on 31 imbalanced and 7 standard data sets.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2017.2676239