Exploring the Best Classification from Average Feature Combination
Joint Authors
Hou, Jian
Karimi, Hamid Reza
Liu, Wei-Xue
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-19
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Feature combination is a powerful approach to improve object classification performance.
While various combination algorithms have been proposed, average combination is almost always selected as the baseline algorithm to be compared with.
In previous work we have found that it is better to use only a sample of the most powerful features in average combination than using all.
In this paper, we continue this work and further show that the behaviors of features in average combination can be integrated into the k-Nearest-Neighbor (kNN) framework.
Based on the kNN framework, we then propose to use a selection based average combination algorithm to obtain the best classification performance from average combination.
Our experiments on four diverse datasets indicate that this selection based average combination performs evidently better than the ordinary average combination, and thus serves as a better baseline.
Comparing with this new and better baseline makes the claimed superiority of newly proposed combination algorithms more convincing.
Furthermore, the kNN framework is helpful in understanding the underlying mechanism of feature combination and motivating novel feature combination algorithms.
American Psychological Association (APA)
Hou, Jian& Liu, Wei-Xue& Karimi, Hamid Reza. 2014. Exploring the Best Classification from Average Feature Combination. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1014317
Modern Language Association (MLA)
Hou, Jian…[et al.]. Exploring the Best Classification from Average Feature Combination. Abstract and Applied Analysis No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1014317
American Medical Association (AMA)
Hou, Jian& Liu, Wei-Xue& Karimi, Hamid Reza. Exploring the Best Classification from Average Feature Combination. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1014317
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1014317