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Coupling K-nearest neighbors with logistic regression in case-based reasoning.

Boris Campillo-Gimenez1, Sahar Bayat, Marc Cuggia

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This study introduces a hybrid approach combining logistic regression (LR) with K-nearest neighbors (K-NN) to enhance case-based reasoning (CBR) classification. The method optimizes K-NN settings using LR residuals, improving classification performance in CBR systems.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Case-based reasoning (CBR) systems rely on similarity functions to address new problems by referencing past cases.
  • The K-nearest neighbors (K-NN) algorithm is commonly employed in CBR to determine the status of new cases based on similar past cases.

Purpose of the Study:

  • To propose and evaluate a novel hybrid approach integrating logistic regression (LR) with K-NN to optimize CBR classification.
  • To enhance the performance of CBR systems by leveraging the utility of past cases identified through LR analysis.

Main Methods:

  • A hybrid model combining logistic regression (LR) and K-nearest neighbors (K-NN) was developed for CBR.
  • LR was used to analyze the knowledge database, with Pearson residuals identifying the utility of past cases for K-NN.
  • Classification performance was compared between the standalone LR model, standalone K-NN, and the hybrid LR-K-NN approach.

Main Results:

  • The Pearson residuals from the LR model effectively informed the utility of cases within the K-NN algorithm.
  • The hybrid LR-K-NN approach demonstrated improved classification performance compared to standalone LR or K-NN.
  • Optimizing K-NN settings using LR-derived information significantly enhanced CBR classification accuracy.

Conclusions:

  • The proposed hybrid LR-K-NN method offers a significant improvement for CBR classification tasks.
  • Utilizing LR residuals provides valuable insights for optimizing K-NN parameters in CBR systems.
  • This approach enhances the efficiency and accuracy of solving new problems by effectively utilizing historical data.