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Improving Classification Accuracy in Hierarchical Trees via Greedy Node Expansion

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(6), pp.113-120
  • DOI : 10.9708/jksci.2024.29.06.113
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : May 7, 2024
  • Accepted : June 5, 2024
  • Published : June 28, 2024

Byungjin Lim 1 Jong Wook Kim 1

1상명대학교

Accredited

ABSTRACT

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

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