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On the Enhancement of Classification Algorithms Using Biased Samples.
- Source :
-
Inteligencia Artificial: Revista Iberoamericana de Inteligencia Artificial . 2019, Vol. 22 Issue 64, p36-46. 11p. - Publication Year :
- 2019
-
Abstract
- Classification algorithms' performance could be enhanced by selecting many representative points to be included in the training sample. In this paper, a new border and rare biased sampling (BRBS) scheme is proposed by assigning each point in the dataset an importance factor. The importance factor of border points and rare points (i.e. points belong to rare classes) is higher than other points. Then the points are selected to be in the training sample depending on these factors. Including these points in the training sample enhances classifiers experience. The results of experiments on 10 UCI machine learning repository datasets prove that the BRBS algorithm outperforms many sampling algorithms and enhanced the performance of several classification algorithms by about 8%. BRBS is proposed to be easy to configure, covering all points space, and generate a unique samples every time it is executed. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*CLASSIFICATION algorithms
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 11373601
- Volume :
- 22
- Issue :
- 64
- Database :
- Academic Search Index
- Journal :
- Inteligencia Artificial: Revista Iberoamericana de Inteligencia Artificial
- Publication Type :
- Academic Journal
- Accession number :
- 139893953
- Full Text :
- https://doi.org/10.4114/intartif.vol22iss64pp36-46