Addressing Missing Data and Data Competitiveness Issues: Transforming Tacit Knowledge into Explicit Form by Fuzzy Inference Learning System
- Publication Type:
- Journal Article
- Citation:
- International Journal of Fuzzy Systems, 2018, 20 (4), pp. 1224 - 1239
- Issue Date:
- 2018-04-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Zakeri2018_Article_AddressingMissingDataAndDataCo.pdf | Published Version | 1.92 MB |
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© 2017, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature. Although we are living in the era of big data, in many real-world applications, being able to access the right set and quantity of data is still a challenging task. One solution to address this drawback is to transform the existing information and knowledge from its tacit form back to data which can be used to simulate and regenerate the required knowledge in different scenarios for further analysis in explicit form. In this paper, we present our developed fuzzy inference-based learning system to achieve this objective. Our proposed framework is based on both conventional fuzzy-based modelling and the adaptive network-based fuzzy inference system (ANFIS) that first transforms the existing tacit information and knowledge into a fuzzy form which is then fed into ANFIS to develop a trained model that regenerates them for analysis purposes. We validate our proposed model and demonstrate its accuracy to estimate the fuel efficiency of heavy duty trucks using real-world data.
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