Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization
Joint Authors
Guo, Qiao
La, Lei
Yang, Dequan
Cao, Qimin
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-24, 24 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-08-15
Country of Publication
Egypt
No. of Pages
24
Main Subjects
Abstract EN
AdaBoost is an excellent committee-based tool for classification.
However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM), neural networks (NN), naïve Bayes, and k-nearest neighbor (kNN).
This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems.
This novel method is more effective.
In addition, it keeps the accuracy advantage of existing AdaBoost.
An adaptive group-based kNN method is proposed in this paper to build more accurate weak classifiers and in this way control the number of basis classifiers in an acceptable range.
To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group-based kNN boosting algorithm (AGkNN-AdaBoost).
We implement AGkNN-AdaBoost in a Chinese text categorization system.
Experimental results showed that the classification algorithm proposed in this paper has better performance both in precision and recall than many other text categorization methods including traditional AdaBoost.
In addition, the processing speed is significantly enhanced than original AdaBoost and many other classic categorization algorithms.
American Psychological Association (APA)
La, Lei& Guo, Qiao& Yang, Dequan& Cao, Qimin. 2012. Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-24.
https://search.emarefa.net/detail/BIM-1029768
Modern Language Association (MLA)
La, Lei…[et al.]. Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization. Mathematical Problems in Engineering No. 2012 (2012), pp.1-24.
https://search.emarefa.net/detail/BIM-1029768
American Medical Association (AMA)
La, Lei& Guo, Qiao& Yang, Dequan& Cao, Qimin. Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-24.
https://search.emarefa.net/detail/BIM-1029768
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1029768