| Autor: |
Ireimis Leguen-deVarona, Julio Madera, Yoan Martínez-López, José Hernández-Nieto |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
EAI Endorsed Transactions on Energy Web, Vol 7, Iss 27 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2032-944X |
| DOI: |
10.4108/eai.13-7-2018.163982 |
| Popis: |
INTRODUCTION: Nowadays, many machine learning tasks involve learning from imbalanced datasets,leading to the miss-classification of the minority class. One of the state-of-the-art approaches to ”solve” thisproblem at the data level is Synthetic Minority Over-sampling Technique (SMOTE) which in turn uses KNearest Neighbors (KNN) algorithm to select and generate new instances.OBJECTIVES: This paper presents SMOTE-Cov, a modified SMOTE that use Covariance Matrix instead ofKNN to balance datasets, with continuous attributes and binary class.METHODS: We implemented two variants SMOTE-CovI, which generates new values within the interval ofeach attribute and SMOTE-CovO, which allows some values to be outside the interval of the attributes.RESULTS: The results show that our approach has a similar performance as the state- of-the-art approaches.CONCLUSION: In this paper, a new algorithm is proposed to generate synthetic instances of the minorityclass, using the Covariance Matrix. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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