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- W2587469129 abstract "Rapid advance of computer technologies in data processing, collection, and storage has provided unparalleled opportunities to expand capabilities i n production, services, communications, and research. However, immense quantities of high-dimensional data renew the challenges to the state-of-the-art data m ining techniques. Artificial Bee Colony (ABC) is a popula r metaheuristic search algorithm used in solving numerous combinatorial optimization problems. Feature Select ion (FS) helps to speed up the process of classificatio n by extracting the relevant and useful information from the dataset. FS is seen as an optimization problem beca use selecting the appropriate feature subset is very im portant. Classifier Ensemble is the best solution for the pi tfall of accuracy lag in a single classifier. We first brief ly introduce the concept of data mining and key components of feature selection. This paper proposed a hybrid approach fo r the feature selection by using artificial bee colony an d particular swarm optimization. The proposed techniq ue is simulated using the WEKA and results show the better performance of the proposed technique." @default.
- W2587469129 created "2017-02-17" @default.
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- W2587469129 date "2014-01-01" @default.
- W2587469129 modified "2023-09-27" @default.
- W2587469129 title "Hybrid Approach for Feature Subset Selection Hybrid Approach for Feature Subset Selection Hybrid Approach for Feature Subset Selection Hybrid Approach for Feature Subset Selection" @default.
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- W2587469129 hasPublicationYear "2014" @default.
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