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Improved Distance Functions for Instance-Based Text Classification.

Khalil El Hindi1, Bayan Abu Shawar2, Reem Aljulaidan1

  • 1Department of Computer Science, King Saud University, Riyadh, Saudi Arabia.

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Summary
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This study introduces novel distance measures for instance-based learning (IBL) in text classification. New measures based on word frequencies and ordinal relationships outperform traditional methods and Naïve Bayesian classifiers.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Instance-based learning (IBL) is a high-performing method for text classification.
  • The effectiveness of IBL heavily relies on the chosen distance function for document similarity.
  • Existing distance measures may not fully exploit the nuances of text data, such as word frequencies and their ordinal relationships.

Purpose of the Study:

  • To evaluate the performance of popular distance measures in text classification.
  • To propose novel distance measures that leverage word frequencies and their ordinal properties.
  • To compare the efficacy of proposed measures against existing methods and Naïve Bayesian classifiers.

Main Methods:

  • Evaluation of popular distance measures for text classification.
  • Development of new distance measures, including Value Distance Metric (VDM) and Inverted Specific-Class Distance Measure (ISCDM) variants.
  • Comparison using the k-Nearest Neighbors (kNN) algorithm on 18 benchmark text classification datasets.
  • Benchmarking against Naïve Bayesian classifiers (MNB, CNB, OVA).

Main Results:

  • Nominal distance metrics yield better text classification results than Euclidean distance.
  • The proposed ISCDM-based measures significantly outperform VDM-based measures.
  • ISCDM effectively utilizes the ordinal nature of term frequencies, leading to more proposed ISCDM variants.
  • kNN with proposed distance measures surpasses Naïve Bayesian classifiers on most datasets.

Conclusions:

  • Novel distance measures enhance the performance of instance-based learning for text classification.
  • ISCDM-based metrics offer a superior approach to document similarity compared to VDM and Euclidean distance.
  • The proposed methods provide a competitive alternative to traditional Naïve Bayesian text classifiers.