Binary k-nearest neighbor for text categorization

• Published in: Online information review Vol. 29; no. 4; pp. 391 - 399
• Main Author: Tan, Songbo
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Group Publishing Limited 01.01.2005; Emerald
• Subjects: Binary system; Classification; Exact sciences and technology; Indexing; Information and communication sciences; Information management; Information processing and retrieval; Information retrieval; Information retrieval. Man machine relationship; Information science. Documentation; Internet; Online information retrieval; Research process. Evaluation; Sciences and techniques of general use; Searches; Studies; Text categorization; Vocabularies & taxonomies; Data handling; Information retrieval; Classification; Nearest neighbour; Classifier; Algorithm; Categorization
• ISSN: 1468-4527; 1468-4535
• DOI: 10.1108/14684520510617839

Purpose - With the ever-increasing volume of text data via the internet, it is important that documents are classified as manageable and easy to understand categories. This paper proposes the use of binary k-nearest neighbour (BKNN) for text categorization.Design methodology approach - The paper describes the traditional k-nearest neighbor (KNN) classifier, introduces BKNN and outlines experiemental results.Findings - The experimental results indicate that BKNN requires much less CPU time than KNN, without loss of classification performance.Originality value - The paper demonstrates how BKNN can be an efficient and effective algorithm for text categorization. Proposes the use of binary k-nearest neighbor (BKNN ) for text categorization.