Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions
Researchers are interested in defining decision support systems that can act in contexts characterized by uncertainty and info-incompleteness. The present study proposes a learning model for assessing the relevance of probability, plausibility, credibility, and possibility opinions in the conditions...
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| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/1/194 |
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| author | Gerardo Iovane Riccardo Emanuele Landi Antonio Rapuano Riccardo Amatore |
| author_facet | Gerardo Iovane Riccardo Emanuele Landi Antonio Rapuano Riccardo Amatore |
| author_sort | Gerardo Iovane |
| collection | DOAJ |
| description | Researchers are interested in defining decision support systems that can act in contexts characterized by uncertainty and info-incompleteness. The present study proposes a learning model for assessing the relevance of probability, plausibility, credibility, and possibility opinions in the conditions above. The solution consists of an Artificial Neural Network acquiring input features related to the considered set of opinions and other relevant attributes. The model provides the weights for minimizing the error between the expected outcome and the ground truth concerning a given phenomenon of interest. A custom loss function was defined to minimize the Mean Best Price Error (MBPE), while the evaluation of football players’ was chosen as a case study for testing the model. A custom dataset was constructed by scraping the Transfermarkt, Football Manager, and FIFA21 information sources and by computing a sentiment score through BERT, obtaining a total of 398 occurrences, of which 85% were employed for training the proposed model. The results show that the probability opinion represents the best choice in conditions of info-completeness, predicting the best price with 0.86 MBPE (0.61% of normalized error), while an arbitrary set composed of plausibility, credibility, and possibility opinions was considered for deciding successfully in info-incompleteness, achieving a confidence score of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.47</mn><mo>±</mo><mn>0.188</mn></mrow></semantics></math></inline-formula> MBPE (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.89</mn><mo>±</mo><mn>0.15</mn></mrow></semantics></math></inline-formula>% of normalized error). The proposed solution provided high performance in predicting the transfer cost of a football player in conditions of both info-completeness and info-incompleteness, revealing the significance of extending the feature space to opinions concerning the quantity to predict. Furthermore, the assumptions of the theoretical background were confirmed, as well as the observations found in the state of the art regarding football player evaluation. |
| first_indexed | 2024-03-10T03:50:08Z |
| format | Article |
| id | doaj.art-3e9bdf0c1e014acda8ba721b243e28d4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:50:08Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3e9bdf0c1e014acda8ba721b243e28d42023-11-23T11:09:10ZengMDPI AGApplied Sciences2076-34172021-12-0112119410.3390/app12010194Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness ConditionsGerardo Iovane0Riccardo Emanuele Landi1Antonio Rapuano2Riccardo Amatore3Department of Computer Science, University of Salerno, 84084 Fisciano, ItalyRigenera S.r.l., Via Aventina 7, 00153 Rome, ItalyDepartment of Computer Science, University of Salerno, 84084 Fisciano, ItalyDepartment of Computer Science, University of Salerno, 84084 Fisciano, ItalyResearchers are interested in defining decision support systems that can act in contexts characterized by uncertainty and info-incompleteness. The present study proposes a learning model for assessing the relevance of probability, plausibility, credibility, and possibility opinions in the conditions above. The solution consists of an Artificial Neural Network acquiring input features related to the considered set of opinions and other relevant attributes. The model provides the weights for minimizing the error between the expected outcome and the ground truth concerning a given phenomenon of interest. A custom loss function was defined to minimize the Mean Best Price Error (MBPE), while the evaluation of football players’ was chosen as a case study for testing the model. A custom dataset was constructed by scraping the Transfermarkt, Football Manager, and FIFA21 information sources and by computing a sentiment score through BERT, obtaining a total of 398 occurrences, of which 85% were employed for training the proposed model. The results show that the probability opinion represents the best choice in conditions of info-completeness, predicting the best price with 0.86 MBPE (0.61% of normalized error), while an arbitrary set composed of plausibility, credibility, and possibility opinions was considered for deciding successfully in info-incompleteness, achieving a confidence score of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2.47</mn><mo>±</mo><mn>0.188</mn></mrow></semantics></math></inline-formula> MBPE (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1.89</mn><mo>±</mo><mn>0.15</mn></mrow></semantics></math></inline-formula>% of normalized error). The proposed solution provided high performance in predicting the transfer cost of a football player in conditions of both info-completeness and info-incompleteness, revealing the significance of extending the feature space to opinions concerning the quantity to predict. Furthermore, the assumptions of the theoretical background were confirmed, as well as the observations found in the state of the art regarding football player evaluation.https://www.mdpi.com/2076-3417/12/1/194decision support systemsuncertaintyinfo-incompletenessmachine learningartificial intelligencefootball market |
| spellingShingle | Gerardo Iovane Riccardo Emanuele Landi Antonio Rapuano Riccardo Amatore Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions Applied Sciences decision support systems uncertainty info-incompleteness machine learning artificial intelligence football market |
| title | Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions |
| title_full | Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions |
| title_fullStr | Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions |
| title_full_unstemmed | Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions |
| title_short | Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions |
| title_sort | assessing the relevance of opinions in uncertainty and info incompleteness conditions |
| topic | decision support systems uncertainty info-incompleteness machine learning artificial intelligence football market |
| url | https://www.mdpi.com/2076-3417/12/1/194 |
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