Ordered weighted averaging operator used to enhance the accuracy of fuzzy predictor based on genetic algorithm Article Swipe
Bindu Garg
,
Md Tabrez Nafis
,
Rohit Garg
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1504/ijista.2018.10012895
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1504/ijista.2018.10012895
In this paper, we have proposed a novel concept to optimise ordered weighted aggregation (OWA) based fuzzy time series predictor (FTSP) using genetic algorithm (GA). Firstly, accurateness of FTSP is enhanced by applying effective method of aggregation on past observations using OWA weights. These weights are determined on the basis of importance of fuzzy set in the system by employing regularly increasing monotonic (RIM) quantifiers. Subsequently, GA is used to optimise membership functions of FTSP by generating its wide range of parameters in the region of time series. Lastly, this model is capable of controlling its performance by varying GA parameters. To assess proposed method, we used dataset of enrolments and outpatient visits, as used by almost all previous research in this domain. Evaluation results indicate coalescing OWA and GA for FTSP significantly reduced mean square error (MSE) and average forecasting error rate (AFER).
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1504/ijista.2018.10012895
- https://doi.org/10.1504/ijista.2018.10012895
- OA Status
- bronze
- Cited By
- 2
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2799853357
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2799853357Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1504/ijista.2018.10012895Digital Object Identifier
- Title
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Ordered weighted averaging operator used to enhance the accuracy of fuzzy predictor based on genetic algorithmWork title
- Type
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articleOpenAlex work type
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enPrimary language
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2018Year of publication
- Publication date
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2018-01-01Full publication date if available
- Authors
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Bindu Garg, Md Tabrez Nafis, Rohit GargList of authors in order
- Landing page
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https://doi.org/10.1504/ijista.2018.10012895Publisher landing page
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https://doi.org/10.1504/ijista.2018.10012895Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://doi.org/10.1504/ijista.2018.10012895Direct OA link when available
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Algorithm, Operator (biology), Fuzzy logic, Genetic algorithm, Computer science, Mathematics, Artificial intelligence, Mathematical optimization, Transcription factor, Repressor, Chemistry, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1, 2020: 1Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.varying | 98 |
| abstract_inverted_index.visits, | 112 |
| abstract_inverted_index.weights | 44 |
| abstract_inverted_index.Firstly, | 25 |
| abstract_inverted_index.applying | 32 |
| abstract_inverted_index.enhanced | 30 |
| abstract_inverted_index.indicate | 125 |
| abstract_inverted_index.optimise | 10, 70 |
| abstract_inverted_index.previous | 118 |
| abstract_inverted_index.proposed | 5, 103 |
| abstract_inverted_index.research | 119 |
| abstract_inverted_index.weighted | 12 |
| abstract_inverted_index.weights. | 42 |
| abstract_inverted_index.algorithm | 23 |
| abstract_inverted_index.effective | 33 |
| abstract_inverted_index.employing | 59 |
| abstract_inverted_index.functions | 72 |
| abstract_inverted_index.monotonic | 62 |
| abstract_inverted_index.predictor | 19 |
| abstract_inverted_index.regularly | 60 |
| abstract_inverted_index.Evaluation | 123 |
| abstract_inverted_index.coalescing | 126 |
| abstract_inverted_index.determined | 46 |
| abstract_inverted_index.enrolments | 109 |
| abstract_inverted_index.generating | 76 |
| abstract_inverted_index.importance | 51 |
| abstract_inverted_index.increasing | 61 |
| abstract_inverted_index.membership | 71 |
| abstract_inverted_index.outpatient | 111 |
| abstract_inverted_index.parameters | 81 |
| abstract_inverted_index.aggregation | 13, 36 |
| abstract_inverted_index.controlling | 94 |
| abstract_inverted_index.forecasting | 140 |
| abstract_inverted_index.parameters. | 100 |
| abstract_inverted_index.performance | 96 |
| abstract_inverted_index.accurateness | 26 |
| abstract_inverted_index.observations | 39 |
| abstract_inverted_index.quantifiers. | 64 |
| abstract_inverted_index.Subsequently, | 65 |
| abstract_inverted_index.significantly | 132 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.67367051 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |