Research on optimization of feature selection algorithm based on neighborhood preservation criterion
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Abstract:
The application of feature selection to deal with "Dimensional Disaster" in multi-label data classification has become an important research direction, we proposed a feature selection algorithm based on neighborhood preservation criterion (NPFS). A similarity preservation expression was constructed by approximating two similarity matrices based on feature subspace and label space. Then, the similarity preservation formulation was extended by the linear approximation to obtain a formulation of the neighborhood relationship preservation, and the importance of the feature subset was evaluated by calculating the neighborhood relationship preserving score (NRPS).A multi-label feature selection algorithm with NRPS was designed in combination with the greedy method(NPFS).The simulation results show that the metrics of average precision, coverage, hamming loss, one-error, ranking loss obtained by the proposed algorithm have been improved compared with those obtained by MMIFS algorithm and MDMR algorithm.
