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Handling veracity of SVM predictions

Marcelo Eduardo Loor Romero (UGent) , Ana Tapia Rosero and Guy De Tré (UGent)
(2024) INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2024, VOL 1. In Lecture Notes in Networks and Systems 1174. p.51-61
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Abstract
Any concern about the veracity of predictions made by artificial intelligence (AI) systems might deter decision makers from using them to support their decisions. To dismiss such concerns in AI systems based on support vector machines (SVMs), in this paper we explore the use of L-grades for dealing with the veracity of SVM predictions and propose a novel method for obtaining those grades. An illustrative example shows how an L-grade can be used for denoting to what extent an SVM prediction can be trusted, as well as how to obtain L-grades inside a binary classification process.
Keywords
Trustworthy artificial intelligence, explainable artificial intelligence, support vector machines, L-grades, Z-numbers, SUPPORT VECTOR MACHINES, RULE EXTRACTION

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MLA
Loor Romero, Marcelo Eduardo, et al. “Handling Veracity of SVM Predictions.” INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2024, VOL 1, vol. 1174, Springer Cham, 2024, pp. 51–61, doi:10.1007/978-3-031-74003-9_5.
APA
Loor Romero, M. E., Tapia Rosero, A., & De Tré, G. (2024). Handling veracity of SVM predictions. INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2024, VOL 1, 1174, 51–61. https://doi.org/10.1007/978-3-031-74003-9_5
Chicago author-date
Loor Romero, Marcelo Eduardo, Ana Tapia Rosero, and Guy De Tré. 2024. “Handling Veracity of SVM Predictions.” In INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2024, VOL 1, 1174:51–61. Springer Cham. https://doi.org/10.1007/978-3-031-74003-9_5.
Chicago author-date (all authors)
Loor Romero, Marcelo Eduardo, Ana Tapia Rosero, and Guy De Tré. 2024. “Handling Veracity of SVM Predictions.” In INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2024, VOL 1, 1174:51–61. Springer Cham. doi:10.1007/978-3-031-74003-9_5.
Vancouver
1.
Loor Romero ME, Tapia Rosero A, De Tré G. Handling veracity of SVM predictions. In: INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2024, VOL 1. Springer Cham; 2024. p. 51–61.
IEEE
[1]
M. E. Loor Romero, A. Tapia Rosero, and G. De Tré, “Handling veracity of SVM predictions,” in INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2024, VOL 1, Lisbon, Portugal, 2024, vol. 1174, pp. 51–61.
@inproceedings{01J3ZHC2APE0SSHPH6YH71NBD1,
  abstract     = {{Any concern about the veracity of predictions made by artificial intelligence (AI) systems might deter decision makers from using them to support their decisions. To dismiss such concerns in AI systems based on support vector machines (SVMs), in this paper we explore the use of L-grades for dealing with the veracity of SVM predictions and propose a novel method for obtaining those grades. An illustrative example shows how an L-grade can be used for denoting to what extent an SVM prediction can be trusted, as well as how to obtain L-grades inside a binary classification process.}},
  author       = {{Loor Romero, Marcelo Eduardo and Tapia Rosero, Ana and De Tré, Guy}},
  booktitle    = {{INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2024, VOL 1}},
  isbn         = {{9783031740022}},
  issn         = {{2367-3370}},
  keywords     = {{Trustworthy artificial intelligence,explainable artificial intelligence,support vector machines,L-grades,Z-numbers,SUPPORT VECTOR MACHINES,RULE EXTRACTION}},
  language     = {{eng}},
  location     = {{Lisbon, Portugal}},
  pages        = {{51--61}},
  publisher    = {{Springer Cham}},
  title        = {{Handling veracity of SVM predictions}},
  url          = {{http://doi.org/10.1007/978-3-031-74003-9_5}},
  volume       = {{1174}},
  year         = {{2024}},
}
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