| Author | Search for: Pizzi, N. J.1; Search for: Pedrycz, W. |
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| Affiliation | - National Research Council of Canada. NRC Institute for Biodiagnostics
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| Format | Text, Article |
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| Conference | Joint 2009 International Fuzzy Systems Association World Congress, IFSA 2009 and 2009 European Society of Fuzzy Logic and Technology Conference, EUSFLAT 2009, July 20-24, 2009, Lisbon, Portugal |
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| Subject | biomedical data; fuzzy logic network; pattern classification; principal component analysis |
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| Abstract | The pattern recognition literature is replete with the use of principal component analysis in the interpretation and analysis of data. However, in the specific case of classification, especially of biomedical patterns, this pre-processing method, which transforms possibly correlated features into a new set of uncorrelated variables, must be used with caution since a principal component, which may account for significant variance in the data, is not necessarily discriminatory. To compensate for this deficiency, we present a novel classification method using an adaptive network of fuzzy logic connectives to select the most discriminatory principal components. We empirically demonstrate the effectiveness of this method using a benchmark combination of a conventional classifier and principal component analysis. |
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| Publication date | 2009 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 23004725 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 38a1fe9f-9227-4b5f-aad0-c4b5c0a09751 |
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| Record created | 2018-12-12 |
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| Record modified | 2020-04-16 |
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