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HMC-ReliefF: Feature ranking for hierarchical multi-label classification
2018
Computer Science and Information Systems
In machine learning, the growing complexity of the available data poses an increased challenge for its analysis. The rising complexity is both in terms of the data becoming more high-dimensional as well as the data having a more intricate structure. This emphasizes the need for developing machine learning algorithms that are able to tackle both the high-dimensionality and the complex structure of the data. Our work in this paper focuses on the development and analysis of the HMC-ReliefF
doi:10.2298/csis170115043s
fatcat:bhwxk4uzzbfstp6nreqwy2tznq