Dimensionality reduction of inputs for a Fuzzy Cognitive Map for obesity problem
| Autor: | Nancy Nadar Selvin, Anuradha Srinivasaraghavan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
Dimensionality reduction 05 social sciences Feature extraction 02 engineering and technology computer.software_genre Fuzzy cognitive map 0202 electrical engineering electronic engineering information engineering Preprocessor 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Algorithm design 050102 behavioral science & comparative psychology Data pre-processing Data mining Cluster analysis computer Curse of dimensionality |
| Zdroj: | 2016 International Conference on Inventive Computation Technologies (ICICT). |
| DOI: | 10.1109/inventive.2016.7830187 |
| Popis: | Data preprocessing is applied in many of the clustering or classification problem to produce output with less processing time and less number of features. Fuzzy Cognitive Maps is a relatively new domain in the field of artificial Intelligence. For some problem like the Obesity the number of inputs can vary enormously. Hence the Dimensionality Reduction technique is applied and the simulations are run with the inputs obtained by after preprocessing. The results show promising application of Dimensionality Methodology to Fuzzy Cognitive Map problems and also the results show that the number of Inputs in FCM can affect the value of the output. |
| Databáze: | OpenAIRE |
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