Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network
Although many classification techniques exist to analyze patterns possessing straightforward characteristics, they tend to fail when the ratio of features to patterns is very large. This “curse of dimensionality” is especially prevalent in many complex, voluminous biomedical datasets acquired using...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Advances in Fuzzy Systems |
| Online Access: | http://dx.doi.org/10.1155/2012/920920 |
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| _version_ | 1850165827055124480 |
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| author | Nick J. Pizzi Witold Pedrycz |
| author_facet | Nick J. Pizzi Witold Pedrycz |
| author_sort | Nick J. Pizzi |
| collection | DOAJ |
| description | Although many classification techniques exist to analyze patterns possessing straightforward characteristics, they tend to fail when the ratio of features to patterns is very large. This “curse of dimensionality” is especially prevalent in many complex, voluminous biomedical datasets acquired using the latest spectroscopic modalities. To address this pattern classification issue, we present a technique using an adaptive network of fuzzy logic connectives to combine class boundaries generated by sets of discriminant functions. We empirically evaluate the effectiveness of this classification technique by comparing it against two conventional benchmark approaches, both of which use feature averaging as a preprocessing phase. |
| format | Article |
| id | doaj-art-e1c4036814784156a9656ba9eb5d16f0 |
| institution | OA Journals |
| issn | 1687-7101 1687-711X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Fuzzy Systems |
| spelling | doaj-art-e1c4036814784156a9656ba9eb5d16f02025-08-20T02:21:38ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2012-01-01201210.1155/2012/920920920920Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant NetworkNick J. Pizzi0Witold Pedrycz1Department of Computer Science, University of Manitoba, Winnipeg MB, R3T 2N2, CanadaDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton AB, T6R 2G7, CanadaAlthough many classification techniques exist to analyze patterns possessing straightforward characteristics, they tend to fail when the ratio of features to patterns is very large. This “curse of dimensionality” is especially prevalent in many complex, voluminous biomedical datasets acquired using the latest spectroscopic modalities. To address this pattern classification issue, we present a technique using an adaptive network of fuzzy logic connectives to combine class boundaries generated by sets of discriminant functions. We empirically evaluate the effectiveness of this classification technique by comparing it against two conventional benchmark approaches, both of which use feature averaging as a preprocessing phase.http://dx.doi.org/10.1155/2012/920920 |
| spellingShingle | Nick J. Pizzi Witold Pedrycz Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network Advances in Fuzzy Systems |
| title | Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network |
| title_full | Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network |
| title_fullStr | Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network |
| title_full_unstemmed | Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network |
| title_short | Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network |
| title_sort | classifying high dimensional patterns using a fuzzy logic discriminant network |
| url | http://dx.doi.org/10.1155/2012/920920 |
| work_keys_str_mv | AT nickjpizzi classifyinghighdimensionalpatternsusingafuzzylogicdiscriminantnetwork AT witoldpedrycz classifyinghighdimensionalpatternsusingafuzzylogicdiscriminantnetwork |