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BIFFOA: A Novel Binary Improved Fruit Fly Algorithm for Feature Selection
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DOI：10.1109/ACCESS.2019.2917502.png)
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Abstract
En 中文
Feature selection is an important method to reduce the number of attributes of high-dimensional data and an essential preprocess work in classification. It eliminates irrelevant, redundant, and noisy features improves the performance of the model and reduces the computational burden. Fruit fly optimization algorithm is a newalgorithm proposed in recent years, which imitates the foraging behavior of fruit fly. To the best of our knowledge, it has not been systematically applied to feature selection. This paper uses the fruit fly optimization algorithm as a search strategy and designs a wrapper-based feature selection method, named binary improved fruit fly optimization algorithm (BIFFOA). Besides, four different strategies based on evolutionary population dynamics (EPD) and new mutation operators are employed to enhance the BIFFOA. The extensive experiments on 25 datasets (see Table 1) show that the performance of the BIFFOA is better than several state-of-the-art algorithms.
Keywords:
Classification
evolutionary population dynamics
feature selection
fruit fly optimization algorithm
Journal
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3.6
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9.8W
Citations:
29.4W
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