Multi-Label Attribute Reduction Based on Variable Precision Fuzzy Neighborhood Rough Set

Abstract

Multi-label attribute reduction as a common dimensionality reduction technique has obtained widely research in recent years. Most existing multi-label attribute reduction methods adopt discretization to deal with mixed data and have strict requirements on the condition of sample classification. However, the process of discretization may lead to information loss, moreover, strict conditions will increase the possibility of a sample classified into a wrong class. Based on this, we construct a multi-label attribute reduction method based on variable precision fuzzy neighborhood rough set. The main motivation is that the variable precision fuzzy neighborhood rough set can process multiple types of data without discretization and tolerate noisy data. Specifically, we first use the parameterized fuzzy neighborhood granule to define the fuzzy decision and decision class of each sample under different labels. Then, the fuzzy decision and decision classes under different labels are fused into the entire multi-label learning space. Finally, a multilabel attribute reduction algorithm is designed according to the defined maximum attribute significance criterion. Our experiments are conducted on a series of multi-label datasets, and the experimental results verify that the proposed algorithm achieves better classification performance than other state-of-the-art comparison algorithms.INDEX TERMS Attribute reduction, multi-label learning, rough set model, fuzzy neighborhood, variable precision.

Innovations to Attribute Reduction of Covering Decision System Based on Conditional Information Entropy

“…Based on this understanding, we could construct an algorithm to compute all the relative reductions. Definition 7 can be found in the literature [16,21,23]. Definition 7.…”

“…Ma [22] constructed a decision tree based on the covering rough set theory. Chen et al [23] got a multi-label attribute reduction algorithm on neighbourhood rough set. Zhang et al [24] developed the belief and plausibility functions from the evidence theory and these are employed to characterise attribute reductions in the covering decision information system.…”

Multi-label feature selection based on fuzzy neighborhood rough sets

“…So Lin et al [47] used different fuzzy relations to construct a multi-label fuzzy rough sets model (MFRS), which estimated the similarity between samples under different labels, and directly evaluated the attributes of multi-label data, solved the problem of low separability about fuzzy similarity and defined the dependency function. But FRS is sensitive to noise, these noisy data will affect the calculation of fuzzy lower approximation and limit their practical application [48]. To solve the above problems, the fuzzy neighborhood rough sets model (FNRS) is designed.…”

“…Wang et al [49] combined NRS with FRS, proposed a feature selection algorithm based on FNRS via dependency to select feature subset. Chen et al [48] designed a multi-label attribute reduction method based on variable precision FNRS, which used parameterized fuzzy neighborhood granule to define the fuzzy decision and decision class, and calculated importance of features using dependency measure, but the reduction based on the positive region does not take into account the influence of the uncertain information in the upper approximation on the importance of the attribute. Inspired by these observations, this paper designs a multi-label feature selection method based on FNRS and the approximation accuracy is introduced into our proposed multi-label feature selection method.…”

An Optimization of Master S-N Curve Fitting Method Based on Improved Neighborhood Rough Set

“…Fneighborhood parallel reduction algorithm for a multi-label decision system is constructed [26]. A multi-label attribute reduction method based on variable precision fuzzy neighborhood rough set is proposed [27]. A semi-supervised reduction algorithm is presented for the feature selection of partially labeled data [28].…”