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Ensemble classifier optimization by reducing input features and base classifiers
conference contribution
posted on 2019-10-30, 00:00 authored by Muhammad Zohaib JanMuhammad Zohaib Jan, Brijesh VermaEnsemble classifier approaches either exploit the input feature space also known as the dataset attributes, or exploit the data sample space, for example Random Forest (RaF) exploits input features whereas Bagging exploits data sample space. Very few ensemble classifier approaches exist that exploit the both. In this paper we propose an ensemble classifier approach that first reduces input feature space by selecting only the significant features from input data that can maximize the classification performance of the ensemble and then optimize the base classifier pool by incorporating an evolutionary algorithm. The proposed approach is evaluated on benchmark datasets from UCI repository. The results are compared with single classifier approaches and existing state of the art ensemble classifier approaches.
Funding
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
History
Start Page
1580End Page
1587Number of Pages
8Start Date
2019-06-10Finish Date
2019-06-13ISBN-13
9781728121536Location
Wellington, New ZealandPublisher
IEEEPlace of Publication
Piscataway, NJFull Text URL
Peer Reviewed
- Yes
Open Access
- No
Author Research Institute
- Centre for Intelligent Systems
Era Eligible
- Yes