Propositional Kernels
The pervasive presence of artificial intelligence (AI) in our everyday life has nourished the pursuit of explainable AI. Since the dawn of AI, logic has been widely used to express, in a human-friendly fashion, the internal process that led an (intelligent) system to deliver a specific output. In th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/23/8/1020 |
| _version_ | 1827685199681421312 |
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| author | Mirko Polato Fabio Aiolli |
| author_facet | Mirko Polato Fabio Aiolli |
| author_sort | Mirko Polato |
| collection | DOAJ |
| description | The pervasive presence of artificial intelligence (AI) in our everyday life has nourished the pursuit of explainable AI. Since the dawn of AI, logic has been widely used to express, in a human-friendly fashion, the internal process that led an (intelligent) system to deliver a specific output. In this paper, we take a step forward in this direction by introducing a novel family of kernels, called Propositional kernels, that construct feature spaces that are easy to interpret. Specifically, Propositional Kernel functions compute the similarity between two binary vectors in a feature space composed of logical propositions of a fixed form. The Propositional kernel framework improves upon the recent Boolean kernel framework by providing more expressive kernels. In addition to the theoretical definitions, we also provide an algorithm (and the source code) to efficiently construct any propositional kernel. An extensive empirical evaluation shows the effectiveness of Propositional kernels on several artificial and benchmark categorical data sets. |
| first_indexed | 2024-03-10T08:49:31Z |
| format | Article |
| id | doaj.art-2b58ed151bc645898320b7acfe056d62 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T08:49:31Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-2b58ed151bc645898320b7acfe056d622023-11-22T07:35:08ZengMDPI AGEntropy1099-43002021-08-01238102010.3390/e23081020Propositional KernelsMirko Polato0Fabio Aiolli1Department of Mathematics, University of Padova, 35143 Padova, ItalyDepartment of Mathematics, University of Padova, 35143 Padova, ItalyThe pervasive presence of artificial intelligence (AI) in our everyday life has nourished the pursuit of explainable AI. Since the dawn of AI, logic has been widely used to express, in a human-friendly fashion, the internal process that led an (intelligent) system to deliver a specific output. In this paper, we take a step forward in this direction by introducing a novel family of kernels, called Propositional kernels, that construct feature spaces that are easy to interpret. Specifically, Propositional Kernel functions compute the similarity between two binary vectors in a feature space composed of logical propositions of a fixed form. The Propositional kernel framework improves upon the recent Boolean kernel framework by providing more expressive kernels. In addition to the theoretical definitions, we also provide an algorithm (and the source code) to efficiently construct any propositional kernel. An extensive empirical evaluation shows the effectiveness of Propositional kernels on several artificial and benchmark categorical data sets.https://www.mdpi.com/1099-4300/23/8/1020propositional kernelsboolean kernelskernel methodscategorical datapropositional logic |
| spellingShingle | Mirko Polato Fabio Aiolli Propositional Kernels Entropy propositional kernels boolean kernels kernel methods categorical data propositional logic |
| title | Propositional Kernels |
| title_full | Propositional Kernels |
| title_fullStr | Propositional Kernels |
| title_full_unstemmed | Propositional Kernels |
| title_short | Propositional Kernels |
| title_sort | propositional kernels |
| topic | propositional kernels boolean kernels kernel methods categorical data propositional logic |
| url | https://www.mdpi.com/1099-4300/23/8/1020 |
| work_keys_str_mv | AT mirkopolato propositionalkernels AT fabioaiolli propositionalkernels |