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- W2379080471 abstract "Features Reduction is a crucial process in knowledge discovery, however to select an optimal subset from feature set is a NP-Hard problem. A neural network based classification module and GA-based property abstraction algorithm are designed and implemented, which takes the separability measurement of feature as the offset to decrease the searching space of GA, as well as combines the tabu-table searching to avoid repeated searching. In addition, anneal selection is introduced to maintain the diversity of populations, and prevent immaturity convergence. An artificial neural network based classification module is designed, and a supervisory clustering method is proposed to reduce its training time. The experimental result shows that this mechanism can reduce the data dimension and improve the accuracy of data classification." @default.
- W2379080471 created "2016-06-24" @default.
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- W2379080471 date "2006-01-01" @default.
- W2379080471 modified "2023-09-22" @default.
- W2379080471 title "Genetic Algorithm Based Features Reduction" @default.
- W2379080471 hasPublicationYear "2006" @default.
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