Paper Info
Title
Decision-Tree Based Hybrid Filter-Wrapping Method for the Fusion of Multiple Feature Sets.
Abstract
This paper proposes a decision-tree based hybrid filter-wrapping method to solve multiple-feature recognition problems. The decision tree is constructed by various feature sets. Each tree node comprises all possibilities of individual feature combinations: the original features, the serial fusion of original features, and the parallel fusion of original features. In order to generate the best discriminate feature set, a two-stage feature searching algorithm is developed. The first stage is a kind of feature filtering method to find out the local optimal individual features in each level of the tree using a LDA-motivated discrimination criterion. The second stage is a global optimal feature vector generation based on a kind of forward wrapping method. In contrast to literature feature fusion methods which considered filter and wrapping separately, our method combines them together. Furthermore, since our method takes all possibilities of feature combinations into consideration, it is more likely to generate the best discriminate feature set than other feature fusion methods. In addition, our method also compensates discrimination if some portions of original features are missing. The effectiveness of our method is evaluated on a 3D dataset. Comparative experimental results show that our method can impressively improve the recognition accuracy and has better performance than existing methods.
Year
Venue
Field
2015
ICIG
Decision tree,Facial recognition system,Feature vector,Feature fusion,Search algorithm,Pattern recognition,Computer science,Filter (signal processing),Fusion,Artificial intelligence,Hybrid filter
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
7
3
Name
Order
Citations
PageRank
Cuicui Zhang100.34
Xuefeng Liang211415.43
Naixue Xiong32413194.61