EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data
Classification problems with an imbalanced class distribution have received an increased amount of attention within the machine learning community over the last decade. They are encountered in a growing number of real-world situations and pose a challenge to standard machine learning techniques. We...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
Elsevier
2016
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/36055/ |
| _version_ | 1848795214196506624 |
|---|---|
| author | Vluymans, Sarah Triguero, Isaac Cornelis, Chris Saeys, Yvan |
| author_facet | Vluymans, Sarah Triguero, Isaac Cornelis, Chris Saeys, Yvan |
| author_sort | Vluymans, Sarah |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Classification problems with an imbalanced class distribution have received an increased amount of attention within the machine learning community over the last decade. They are encountered in a growing number of real-world situations and pose a challenge to standard machine learning techniques. We propose a new hybrid method specifically tailored to handle class imbalance, called EPRENNID. It performs an evolutionary prototype reduction focused on providing diverse solutions to prevent the method from overfitting the training set. It also allows us to explicitly reduce the underrepresented class, which the most common preprocessing solutions handling class imbalance usually protect. As part of the experimental study, we show that the proposed prototype reduction method outperforms state-of-the-art preprocessing techniques. The preprocessing step yields multiple prototype sets that are later used in an ensemble, performing a weighted voting scheme with the nearest neighbor classifier. EPRENNID is experimentally shown to significantly outperform previous proposals. |
| first_indexed | 2025-11-14T19:28:32Z |
| format | Article |
| id | nottingham-36055 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:28:32Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-360552020-05-04T18:27:37Z https://eprints.nottingham.ac.uk/36055/ EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data Vluymans, Sarah Triguero, Isaac Cornelis, Chris Saeys, Yvan Classification problems with an imbalanced class distribution have received an increased amount of attention within the machine learning community over the last decade. They are encountered in a growing number of real-world situations and pose a challenge to standard machine learning techniques. We propose a new hybrid method specifically tailored to handle class imbalance, called EPRENNID. It performs an evolutionary prototype reduction focused on providing diverse solutions to prevent the method from overfitting the training set. It also allows us to explicitly reduce the underrepresented class, which the most common preprocessing solutions handling class imbalance usually protect. As part of the experimental study, we show that the proposed prototype reduction method outperforms state-of-the-art preprocessing techniques. The preprocessing step yields multiple prototype sets that are later used in an ensemble, performing a weighted voting scheme with the nearest neighbor classifier. EPRENNID is experimentally shown to significantly outperform previous proposals. Elsevier 2016-12-05 Article PeerReviewed Vluymans, Sarah, Triguero, Isaac, Cornelis, Chris and Saeys, Yvan (2016) EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data. Neurocomputing, 216 . pp. 596-610. ISSN 0925-2312 Imbalanced data; Prototype selection; Prototype generation; Differential evolution; Nearest neighbor http://www.sciencedirect.com/science/article/pii/S0925231216308669 doi:10.1016/j.neucom.2016.08.026 doi:10.1016/j.neucom.2016.08.026 |
| spellingShingle | Imbalanced data; Prototype selection; Prototype generation; Differential evolution; Nearest neighbor Vluymans, Sarah Triguero, Isaac Cornelis, Chris Saeys, Yvan EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data |
| title | EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data |
| title_full | EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data |
| title_fullStr | EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data |
| title_full_unstemmed | EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data |
| title_short | EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data |
| title_sort | eprennid: an evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data |
| topic | Imbalanced data; Prototype selection; Prototype generation; Differential evolution; Nearest neighbor |
| url | https://eprints.nottingham.ac.uk/36055/ https://eprints.nottingham.ac.uk/36055/ https://eprints.nottingham.ac.uk/36055/ |