Feature Selection Research For Multi-label And Weak-label Based On Fuzzy Entroy

Nowadays,with the rapid development of computer science and Internet,not only the amount of data is increasing,but also the form of data is becoming more and more complex.So intelligent processing of data is becoming more and more important.Among them,pattern recognition,data mining,machine learning and deep learning have become the main way of processing and mining data.In the face of more and more complex and diverse data,data reduction plays a more and more important role.Multi label data is an extension of traditional single labeled data,and the form of data becomes more complex.Meanwhile,missing markers in multiple tags is also an important problem.Therefore,feature selection for multiple and weak label date is also important.Traditional rough set theory is an effective tool for dealing with uncertainty.It has been widely applied in feature selection,but it can deal with discrete data only.After the emergence of fuzzy rough sets,the problem is solved,and the fuzzy information theory is extended,including fuzzy entropy and fuzzy mutual information.The feature selection algorithm based on traditional information theory has been studied a lot.However,fuzzy information theory based multi label feature selection algorithm is relatively few,which can directly handle numerical data and hybrid data.Therefore,we will use fuzzy rough sets to deal with the problem of feature selection in multi label data.At the same time,a new multi label feature selection algorithm is proposed in view of the characteristics of multi label data.At the same time,multiple label with missing values is a common problem.In view of this situation,combined with the way of missing values in incomplete information system of rough set,the problem of feature selection under multi label with missing values is also dealt with.? Based on the fuzzy information theory and the correlation between labels,a multi label feature selection algorithm is proposed,and the effect analysis is carried out by the experiment.? Based on the feature correlation,a multi label feature selection algorithm which can remove redundant features is proposed,and the effect analysis is carried out through experiments.? Combined with the way of processing missing values in incomplete information system,a multi label feature selection algorithm under weak label is proposed,and the effect of different processing methods and the influence of missing rate on algorithms are compared through experiments.