A design framework for hierarchical ensemble of multiple feature extractors and multiple classifiers. (April 2016)

• Record Type: Journal Article
• Authors: Kim, Kyounghoon; Lin, Helin; Choi, Jin Young; Choi, Kiyoung
• Is Part Of: Pattern recognition. Volume 52(2016:Apr.)
• Page Start: 1
• Page End: 16
• Publication Date: 2016-04
• Subjects: Ensemble of detection systems -- Multiple feature extractors -- Multiple classifiers -- Pedestrian detection -- Reinforcement learning -- Bayesian network; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.11.006
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: It is well-known that ensemble of classifiers can achieve higher accuracy compared to a single classifier system. This paper pays attention to ensemble systems consisting of multiple feature extractors and multiple classifiers (MFMC). However, MFMC increases the system complexity dramatically, leading to a highly complex combinatorial optimization problem. In order to overcome the complexity while exploiting the diversity of MFMC, we suggest in this paper a hierarchical ensemble of MFMC and its optimizing framework. By constructing local groups of feature extractors and classifiers and then combining them as a global group, the approach achieves a better scalability. Both reinforcement machine learning and Bayesian networks are adopted to enhance the accuracy. We apply the proposed method to vision based pedestrian detection and recognition of handwritten numerals. Experimental results show that the proposed framework outperforms the previous ensemble methods in terms of accuracy. Highlights: Optimization of MFMC (multiple feature-extractor, multiple classifier) systems. Presentation of a general design framework for an ensemble of MFMC. Proposing a hierarchical approach for reducing the complexity of MFMC optimization. Proposing a new approach that integrates reinforcement learning and Bayesian network. Experimental results show that the proposed framework outperforms previous approaches.