A comparison of Lucene search queries evolved as text classifiers

HIRSCH, Laurence and BRUNSDON, Teresa (2018). A comparison of Lucene search queries evolved as text classifiers. Applied Artificial Intelligence, 32 (7-8), 768-784.

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Official URL: https://www.tandfonline.com/doi/full/10.1080/08839...
Link to published version:: https://doi.org/10.1080/08839514.2018.1506972

Abstract

In this article, we use a genetic algorithm to evolve seven different types of Lucene search query with the objective of generating accurate and readable text classifiers. We compare the effectiveness of each of the different types of query using three commonly used text datasets. We vary the number of words available for classification and compare results for 4, 8, and 16 words per category. The generated queries can also be viewed as labels for the categories and there is a benefit to a human analyst in being able to read and tune the classifier. The evolved queries also provide an explanation of the classification process. We consider the consistency of the classifiers and compare their performance on categories of different complexities. Finally, various approaches to the analysis of the results are briefly explored.

Item Type: Article
Uncontrolled Keywords: text classification genetic algorithm search query lucene
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
Identification Number: https://doi.org/10.1080/08839514.2018.1506972
Depositing User: Laurence Hirsch
Date Deposited: 21 Aug 2018 12:29
Last Modified: 19 Nov 2018 10:30
URI: http://shura.shu.ac.uk/id/eprint/22330

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