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Abstract
Fuzzy rule interpolation (FRI) strongly supports approximate inference when a new observation matches no rules, through selecting and subsequently interpolating appropriate rules close to the observation from the given (sparse) rule base. Traditional ways of implementing the critical rule selection process are typically based on the exploitation of Euclidean distances between the observation and rules. It is conceptually straightforward for implementation but applying this distance metric may systematically lead to inferior results because it fails to reflect the variations of the relevance or significance levels among different domain features. To address this important issue, a novel transformation-based FRI approach is presented, on the basis of utilizing the Mahalanobis distance metric. The new FRI method works by transforming a given sparse rule base into a coordinates system where the distance between instances of the same category becomes closer while that between different categories becomes further apart. In so doing, when an observation is present that matches no rules, the most relevant neighboring rules to implement the required interpolation are more likely to be selected. Following this, the scale and move factors within the classical transformation-based FRI procedure are also modified by Choquet integral. Systematic experimental investigation over a range of classification problems demonstrates that the proposed approach remarkably outperforms the existing state-of-the-art FRI methods in both accuracy and efficiency.
Original language | English |
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Pages (from-to) | 1083-1097 |
Number of pages | 15 |
Journal | IEEE Transactions on Fuzzy Systems |
Volume | 31 |
Issue number | 4 |
Early online date | 29 Jul 2022 |
DOIs | |
Publication status | Published - 01 Apr 2023 |
Keywords
- Approximate inference
- choquet integral
- choquet integral approximate inference
- Cognition
- Euclidean distance
- fuzzy rule interpolation
- Fuzzy sets
- Interpolation
- mahalanobis distance
- Measurement
- Shape
- Systematics
- transformation -based FRI,
- transformation -based FRI.
- Choquet integral
- transformation-based FRI
- Mahalanobis distance
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Dive into the research topics of 'Transformation-Based Fuzzy Rule Interpolation With Mahalanobis Distance Measures Supported by Choquet Integral'. Together they form a unique fingerprint.Projects
- 1 Finished
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Ser Cymru: Reconstruction of Missing Information in Optical Remote Sensing Images Based on Deep Learning and Knowledge Interpolation
01 Oct 2020 → 28 Feb 2023
Project: Externally funded research