- Title
- FSMEC: a feature selection method based on the minimum spanning tree and evolutionary computation
- Creator
- Zaher, Amer Abu; Berretta, Regina; Arefin, Ahmed Shamsul; Moscato, Pablo
- Relation
- 13th Australasian Data Mining Conference (AusDM 2015). Proceedings of the 13th Australasian Data Mining Conference (Sydney 8-9 August, 2015) p. 129-139
- Relation
- http://crpit.com/PublishedPapers.html
- Publisher
- Australian Computer Society (ACS)
- Resource Type
- conference paper
- Date
- 2015
- Description
- In feature selection we aim at reducing the dimensionality of a dataset by excluding characteristics that do not compromise, and potentially enhance, the classification of a set of samples. We present a new type of supervised and multivariate feature selection approach that works by constructing proximity graphs in such a way that the number of edges connecting samples from different classes is minimised. We present this general idea using the Minimum Spanning Tree as a proximity graph and an Evolutionary Algorithm approach is used to search for a feature subset. We compare the performance of our algorithm against other feature selection methods, (alpha,beta)-k-Feature Set, and a ranking-based feature selection method, based on the use of CM1-scores. We employ two publicly available real-world datasets (one with training and test variants). The classification accuracies have been evaluated using a total of 49 methods from an open source data mining and machine learning package WEKA.
- Subject
- feature selection; evolutionary algorithm; proximity graph; minimum spanning tree
- Identifier
- http://hdl.handle.net/1959.13/1315220
- Identifier
- uon:22915
- Identifier
- ISBN:9781921770180
- Language
- eng
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