Publicação
Oversampling for imbalanced learning based on k-means and smote
| dc.contributor.advisor | Bação, Fernando José Ferreira Lucas | |
| dc.contributor.author | Last, Felix | |
| dc.date.accessioned | 2018-02-22T16:44:00Z | |
| dc.date.available | 2018-02-22T16:44:00Z | |
| dc.date.issued | 2018-02-05 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics | pt_PT |
| dc.description.abstract | Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification algorithm. Such techniques, called oversamplers, modify the training data, allowing any classifier to be used with class-imbalanced datasets. Many algorithms have been proposed for this task, but most are complex and tend to generate unnecessary noise. This work presents a simple and effective oversampling method based on k-means clustering and SMOTE oversampling, which avoids the generation of noise and effectively overcomes imbalances between and within classes. Empirical results of extensive experiments with 71 datasets show that training data oversampled with the proposed method improves classification results. Moreover, k-means SMOTE consistently outperforms other popular oversampling methods. An implementation is made available in the python programming language. | pt_PT |
| dc.identifier.tid | 201852080 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/31042 | |
| dc.language.iso | eng | pt_PT |
| dc.subject | Class-imbalanced learning | pt_PT |
| dc.subject | Oversampling | pt_PT |
| dc.subject | Classification | pt_PT |
| dc.subject | Clustering | pt_PT |
| dc.subject | Supervised learning | pt_PT |
| dc.title | Oversampling for imbalanced learning based on k-means and smote | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Métodos Analíticos Avançados | pt_PT |