A first attempt on global evolutionary undersampling for imbalanced big data
The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are involved as a key step. Existing solutions typicall...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2017
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| Online Access: | https://eprints.nottingham.ac.uk/44071/ |
| _version_ | 1848796830483087360 |
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| author | Triguero, Isaac Galar, M. Bustince, H. Herrera, Francisco |
| author_facet | Triguero, Isaac Galar, M. Bustince, H. Herrera, Francisco |
| author_sort | Triguero, Isaac |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are involved as a key step. Existing solutions typically follow a divide-and-conquer approach in which the data is split into several chunks that are addressed individually. Next, the partial knowledge acquired from every slice of data is aggregated in multiple ways to solve the entire problem. However, these approaches are missing a global view of the data as a whole, which may result in less accurate models.
In this work we carry out a first attempt on the design of a global evolutionary undersampling model for imbalanced classification problems. These are characterised by having a highly skewed distribution of classes in which evolutionary models are being used to balance it by selecting only the most relevant data. Using Apache Spark as big data technology, we have introduced a number of variations to the well-known CHC algorithm to work very large chromosomes and reduce the costs associated to fitness evaluation. We discuss some preliminary results, showing the great potential of this new kind of evolutionary big data model. |
| first_indexed | 2025-11-14T19:54:13Z |
| format | Conference or Workshop Item |
| id | nottingham-44071 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:54:13Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-440712020-05-04T18:54:30Z https://eprints.nottingham.ac.uk/44071/ A first attempt on global evolutionary undersampling for imbalanced big data Triguero, Isaac Galar, M. Bustince, H. Herrera, Francisco The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are involved as a key step. Existing solutions typically follow a divide-and-conquer approach in which the data is split into several chunks that are addressed individually. Next, the partial knowledge acquired from every slice of data is aggregated in multiple ways to solve the entire problem. However, these approaches are missing a global view of the data as a whole, which may result in less accurate models. In this work we carry out a first attempt on the design of a global evolutionary undersampling model for imbalanced classification problems. These are characterised by having a highly skewed distribution of classes in which evolutionary models are being used to balance it by selecting only the most relevant data. Using Apache Spark as big data technology, we have introduced a number of variations to the well-known CHC algorithm to work very large chromosomes and reduce the costs associated to fitness evaluation. We discuss some preliminary results, showing the great potential of this new kind of evolutionary big data model. 2017-07-07 Conference or Workshop Item PeerReviewed Triguero, Isaac, Galar, M., Bustince, H. and Herrera, Francisco (2017) A first attempt on global evolutionary undersampling for imbalanced big data. In: IEEE Congress on Evolutionary Computation (CEC 2017), 5-8 Jun 2017, San Sebastian, Spain. http://ieeexplore.ieee.org/document/7969553/ |
| spellingShingle | Triguero, Isaac Galar, M. Bustince, H. Herrera, Francisco A first attempt on global evolutionary undersampling for imbalanced big data |
| title | A first attempt on global evolutionary undersampling for
imbalanced big data |
| title_full | A first attempt on global evolutionary undersampling for
imbalanced big data |
| title_fullStr | A first attempt on global evolutionary undersampling for
imbalanced big data |
| title_full_unstemmed | A first attempt on global evolutionary undersampling for
imbalanced big data |
| title_short | A first attempt on global evolutionary undersampling for
imbalanced big data |
| title_sort | first attempt on global evolutionary undersampling for
imbalanced big data |
| url | https://eprints.nottingham.ac.uk/44071/ https://eprints.nottingham.ac.uk/44071/ |