Incremental Feature Selection Algorithm for Dynamic Partially Labeled Hybrid Data

Abstract

Many real-world data sets are hybrid data consisting of symbolic,numerical and missing features.For the decision labels of hybrid data,it costs much labor and it is expensive to acquire the decision labels of all data,thus the partially labeled data is generated.Meanwhile,the data in real-world applications change dynamically,i.e.,the feature set is added into and deleted from the feature sets dynamically with different requirements.In this paper,according to the characteristics of high-dimensional,partial labeled and dynamic for the hybrid data,the incremental feature selection algorithms are proposed.Firstly,the information granularity is used to analyze the feature significance for partially labeled hybrid data.Then,the incremental updating mechanisms for information granularity are proposed with the variation of a feature set.On this basis,the incremental feature selection algorithms are proposed for the partially labeled hybrid data.Finally,extensive experimental results on UCI data set demonstrate that the proposed algorithms are feasible and efficient.

Keywords

• hybrid data|partially labeled|incremental learning|information granularity|feature selection