Online feature selection for hierarchical classification learning based on improved ReliefF
نویسندگان
چکیده
Abstract In hierarchical classification learning, the feature space of data has high dimensionality and is unknown with emergent features. To solve above problems, we propose an online selection algorithm based on adaptive ReliefF. Firstly, ReliefF adaptively improved via using density information instances around target sample, making it unnecessary to prespecify parameters. Secondly, relationship between classes used, a new method for calculating weight defined. Then, correlation analysis interaction designed. Finally, redundancy, scaled by features in order achieve dynamic updating redundancy. A large number experiments verify effectiveness proposed algorithm.
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ژورنال
عنوان ژورنال: Concurrency and Computation: Practice and Experience
سال: 2023
ISSN: ['1532-0634', '1532-0626']
DOI: https://doi.org/10.1002/cpe.7844