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Redundancy Is Not Necessarily Detrimental in Classification Problems
- Source :
- Mathematics, Volume 9, Issue 22, Mathematics, Vol 9, Iss 2899, p 2899 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- In feature selection, redundancy is one of the major concerns since the removal of redundancy in data is connected with dimensionality reduction. Despite the evidence of such a connection, few works present theoretical studies regarding redundancy. In this work, we analyze the effect of redundant features on the performance of classification models. We can summarize the contribution of this work as follows: (i) develop a theoretical framework to analyze feature construction and selection, (ii) show that certain properly defined features are redundant but make the data linearly separable, and (iii) propose a formal criterion to validate feature construction methods. The results of experiments suggest that a large number of redundant features can reduce the classification error. The results imply that it is not enough to analyze features solely using criteria that measure the amount of information provided by such features.
- Subjects :
- Computer science
General Mathematics
Dimensionality reduction
Feature selection
computer.software_genre
Measure (mathematics)
feature selection
classification
Computer Science (miscellaneous)
Redundancy (engineering)
Feature (machine learning)
QA1-939
Data mining
feature construction
Engineering (miscellaneous)
computer
Selection (genetic algorithm)
Linear separability
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 22277390
- Database :
- OpenAIRE
- Journal :
- Mathematics
- Accession number :
- edsair.doi.dedup.....619179ed39c22f291954ce269365c192
- Full Text :
- https://doi.org/10.3390/math9222899