LPDA: A new classification method based on linear programming

The search of separation hyperplanes is an efficient way to find rules with classification purposes. This paper presents an alternative mathematical programming formulation to existing methods to find a discriminant hyperplane. The hyperplane H is found by minimizing the sum of all the distances to...

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Bibliographic Details
Published in:PloS one 2022-07, Vol.17 (7), p.e0270403-e0270403
Main Authors: Nueda, María J, Gandía, Carmen, Molina, Mariola D
Format: Article
Language:English
Subjects:
Analysis
Centroids
Cervical cancer
Classification
Computer and Information Sciences
Convexity
Data mining
Datasets
Discriminant analysis
Evaluation
Hyperplanes
Linear programming
Machine learning
Mathematical programming
Medicine and Health Sciences
Methods
Optimization
Optimization techniques
Physical Sciences
Research and Analysis Methods
Support vector machines
Variables
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Summary:The search of separation hyperplanes is an efficient way to find rules with classification purposes. This paper presents an alternative mathematical programming formulation to existing methods to find a discriminant hyperplane. The hyperplane H is found by minimizing the sum of all the distances to the area assigned to the group each individual belongs to. It results in a convex optimization problem for which we find an equivalent linear programming problem. We demonstrate that H exists when the centroids of the two groups are not equal. The method is effective dealing with low and high dimensional data where reduction of the dimension is proposed to avoid overfitting problems. We show the performance of this approach with different data sets and comparisons with other classifications methods. The method is called LPDA and it is implemented in a R package available in
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0270403