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- W3207642145 abstract "We propose a novel hybrid metaheuristic algorithm for optimal selection of features based on simulated annealing (SA), Hybrid Particle Swarm Optimization (HPSO), and Fuzzy K-Means (FKM) called FUFPS to optimally select the features which are more relevant to classify the instances in the dataset. FUFPS is designed to maximize the accuracy of the classifier which considers fitness measures such as intra-cluster similarity and inter-cluster similarity. Feature selection is a challenging task in Analytics as a Service (AaaS) where the dataset has much information that may or may not necessary to identify the pattern. Thus, the domain of data analytics requires a novel mechanism that can optimally select the features which are optimal enough to perform classification. Our proposed system uses various benchmarking datasets intending to select optimal features which can maximize accuracy, F-score and minimize the root mean square error and false-positive rate. The proposed FUFPS is tested and evaluated with other metaheuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO) and the results are proven to be convincing." @default.
- W3207642145 created "2021-10-25" @default.
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- W3207642145 date "2021-01-01" @default.
- W3207642145 modified "2023-09-27" @default.
- W3207642145 title "A Novel Hybrid Feature Selection Algorithm for Optimal Provisioning of Analytics as a Service" @default.
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- W3207642145 doi "https://doi.org/10.1007/978-981-16-2709-5_39" @default.
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