Paper Info
Title
Incremental approaches for feature selection from dynamic data with the variation of multiple objects.
Abstract
Owing to the dynamic characteristics of data in the big data era, multiple objects of a decision system often vary with time when new information arrives in real-world applications. However, many feature selection algorithms are designed for static decision systems, and some dynamic feature selection algorithms treat the variation of multiple objects as the cumulative variation of a single object. In an environment where multiple objects vary with time, these algorithms are often time-consuming. Therefore, strategic behaviors need to be reinforced to improve the efficiency of feature selection. Incremental updating is an efficient technique, which can be applied to deal with dynamic learning tasks because it can make use of previous knowledge to obtain new knowledge. In this paper, we focus on the incremental updating to select a new feature subset with the variation of multiple objects. First, the dependency function is updated in an incremental manner to evaluate the quality of candidate features. Then two incremental feature selection algorithms are developed when multiple objects are added to or deleted from a decision system. Experiments on different UCI data sets show that the proposed algorithms can select new feature subset in much less computational time and do not lose the classification performance when compared with other algorithms.
Year
DOI
Venue
2019
10.1016/j.knosys.2018.08.028
Knowledge-Based Systems
Keywords
Field
DocType
Feature selection,Attribute reduction,Incremental algorithm,Rough sets,Dynamic data
Data mining,Dynamic learning,Data set,Feature selection,Computer science,Decision system,Dynamic data,Artificial intelligence,Big data,Machine learning
Journal
Volume
ISSN
Citations 
163
0950-7051
6
PageRank 
References 
Authors
0.40
41
3
Name
Order
Citations
PageRank
Wenhao Shu112311.98
Wenbin Qian2150.80
Yonghong Xie312214.43