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A Study on Different Text Representation Methods for the Negative Selection Algorithm

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Distributed Computing and Artificial Intelligence, 19th International Conference (DCAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 583))

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Abstract

Unstructured data, such as text, usually have to be structured before standard machine learning classifiers are applied. In such cases, different representation schemes can be used, such as Bag of Words, the Linguistic Inquiry and Word Count (LIWC), Part-of-Speech Tagging (POS Tagging), and others. The Negative Selection Algorithm (NSA) was designed with inspiration in the immune system to solve binary classification problems, more specifically anomaly detection. This paper investigates the performance of various text representation schemes as input to the NSA. Three different datasets and text representation methods are used, and the results are presented in terms of Accuracy and False Positive Rate.

Supported by FAPESP, CNPq and MackPesquisa.

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Authors and Affiliations

  1. Natural Computing and Machine Learning Laboratory (LCoN), Computing and Informatics Faculty, Mackenzie Presbyterian University, São Paulo, SP, 01302-907, Brazil

    Matheus A. Ferraria, Vinicius A. Ferraria & Leandro Nunes de Castro

Authors
  1. Matheus A. Ferraria
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  2. Vinicius A. Ferraria
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  3. Leandro Nunes de Castro
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Corresponding author

Correspondence to Matheus A. Ferraria .

Editor information

Editors and Affiliations

  1. Hiroshima University, Hiroshima, Japan

    Sigeru Omatu

  2. King Abdulaziz University, Jeddah, Saudi Arabia

    Rashid Mehmood

  3. Kielce University of Technology, Kielce, Poland

    Pawel Sitek

  4. Palazzo Camponeschi, University of L'Aquila, L'Aquila, Italy

    Serafino Cicerone

  5. BISITE, Edificio I+D+i, University of Salamanca, Salamanca, Spain

    Sara Rodríguez

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Ferraria, M.A., Ferraria, V.A., de Castro, L.N. (2023). A Study on Different Text Representation Methods for the Negative Selection Algorithm. In: Omatu, S., Mehmood, R., Sitek, P., Cicerone, S., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 583. Springer, Cham. https://doi.org/10.1007/978-3-031-20859-1_30

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