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BY 4.0 license Open Access Published by De Gruyter Open Access August 8, 2019

Efficient Hyperparameter Tuning with Grid Search for Text Categorization using kNN Approach with BM25 Similarity

  • Raji Ghawi EMAIL logo and Jürgen Pfeffer
From the journal Open Computer Science

Abstract

In machine learning, hyperparameter tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Several approaches have been widely adopted for hyperparameter tuning, which is typically a time consuming process. We propose an efficient technique to speed up the process of hyperparameter tuning with Grid Search. We applied this technique on text categorization using kNN algorithm with BM25 similarity, where three hyperparameters need to be tuned. Our experiments show that our proposed technique is at least an order of magnitude faster than conventional tuning.

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Received: 2019-02-20
Accepted: 2019-07-18
Published Online: 2019-08-08

© 2019 Raji Ghawi et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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