Artificial Intelligence

    

Graph Similarity Metric for Modifying K Nearest Neighbor for Classifying Texts

Authors: Taeho Jo

This article proposes the modified KNN (K Nearest Neighbor) algorithm which receives a graph as its input data and is applied tothe text categorization. The graph is more graphical for representing a word and the synergy effect between the text categorization and the word categorization is expected by combining them with each other. In this research, we propose the similaritymetric between two graphs representing words, modify the KNN algorithm by replacing the exiting similarity metric by the proposedone, and apply it to the text categorization. The proposed KNN is empirically validated as the better approach in categorizing texts in news articles and opinions. In this article, a word is encoded into a weighted and undirected graph and it is represented into a list of edges.

Comments: 13 Pages.

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Submission history

[v1] 2024-05-31 02:38:35

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