Reinforcement Post-Processing and Feedback Algorithm for Optimal Combination in Bottom-Up Hierarchical Classification 


Vol. 17,  No. 2, pp. 139-148, Apr.  2010
10.3745/KIPSTB.2010.17.2.139


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  Abstract

This paper shows a reinforcement post-processing method and feedback algorithm for improvement of assigning method in classification. Especially, we focused on complex documents that are generally considered to be hard to classify. A basis factors in traditional classification system are training methodology, classification models and features of documents. The classification problem of the documents containing shared features and multiple meanings, should be deeply mined or analyzed than general formatted data. To address the problems of these document, we proposed a method to expand classification scheme using decision boundary detected automatically in our previous studies. The assigning method that a document simply decides to the top ranked category, is a main factor that we focus on. In this paper, we propose a post-processing method and feedback algorithm to analyze the relevance of ranked list. In experiments, we applied our post-processing method and one time feedback algorithm to complex documents. The experimental results show that our system does not need to change the classification algorithm itself to improve the accuracy and flexibility.

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[IEEE Style]

Y. J. Choi and S. S. Park, "Reinforcement Post-Processing and Feedback Algorithm for Optimal Combination in Bottom-Up Hierarchical Classification," The KIPS Transactions:PartB , vol. 17, no. 2, pp. 139-148, 2010. DOI: 10.3745/KIPSTB.2010.17.2.139.

[ACM Style]

Yun Jeong Choi and Seung Soo Park. 2010. Reinforcement Post-Processing and Feedback Algorithm for Optimal Combination in Bottom-Up Hierarchical Classification. The KIPS Transactions:PartB , 17, 2, (2010), 139-148. DOI: 10.3745/KIPSTB.2010.17.2.139.

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