An Iterated Greedy Algorithm for Improving the Generation of Synthetic Patterns in Imbalanced Learning

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An Iterated Greedy Algorithm for Improving the Generation of Synthetic Patterns in Imbalanced Learning

Javier Maestre-Garcia, F; Garcia-Martinez, C; Perez-Ortiz, M; Antonio Gutierrez, P; (2017) An Iterated Greedy Algorithm for Improving the Generation of Synthetic Patterns in Imbalanced Learning. In: Rojas, I and Joya, G and Catala, A, (eds.) Advances in Computational Intelligence (Proceedings Part 2). (pp. pp. 513-524). Springer: Cham, Switzerland. Green open access

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Abstract

Real-world classification datasets often present a skewed distribution of patterns, where one or more classes are under-represented with respect to the rest. One of the most successful approaches for alleviating this problem is the generation of synthetic minority samples by convex combination of available ones. Within this framework, adaptive synthetic (ADASYN) sampling is a relatively new method which imposes weights on minority examples according to their learning complexity, in such a way that difficult examples are more prone to be oversampled. This paper proposes an improvement of the ADASYN method, where the learning complexity of these patterns is also used to decide which sample of the neighbourhood is selected. Moreover, to avoid suboptimal results when performing the random convex combination, this paper explores the application of an iterative greedy algorithm which refines the synthetic patterns by repeatedly replacing a part of them. For the experiments, six binary datasets and four over-sampling methods are considered. The results show that the new version of ADASYN leads to more robust results and that the application of the iterative greedy metaheuristic significantly improves the quality of the generated patterns, presenting a positive effect on the final classification model.

Type:	Proceedings paper
Title:	An Iterated Greedy Algorithm for Improving the Generation of Synthetic Patterns in Imbalanced Learning
Event:	14th International Work-Conference on Artificial Neural Networks (IWANN 2017), 14-16 June 2017, Cadiz, Spain
Location:	Cadiz, SPAIN
Dates:	14 June 2017 - 16 June 2017
ISBN-13:	978-3-319-59146-9
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1007/978-3-319-59147-6_44
Publisher version:	https://link.springer.com/book/10.1007/978-3-319-5...
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Over-sampling, imbalanced classification, ADASYN, iterative greedy algorithm, metaheuristics
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10069046