Fuzzy c-Means Clustering-Based Novel Threshold Criteria for Outlier Detection in Electronic Nose
| Autor: | Prabha Verma, Mousumi Sinha, Siddhartha Panda |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Electronic nose
business.industry Computer science 010401 analytical chemistry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Context (language use) 01 natural sciences Fuzzy logic 0104 chemical sciences ComputingMethodologies_PATTERNRECOGNITION parasitic diseases Outlier population characteristics Anomaly detection Artificial intelligence Electrical and Electronic Engineering Cluster analysis business Instrumentation geographic locations health care economics and organizations |
| Zdroj: | IEEE Sensors Journal. 21:1975-1981 |
| ISSN: | 2379-9153 1530-437X |
| Popis: | The presence of outliers deteriorates the overall performance of an electronic nose and, thus their detection and removal is critical to achieve the optimum discrimination ability among the subjected volatile organic compounds/gases. This article reports a semi supervised fuzzy ${c}$ -means clustering based outlier detection method in the context of an electronic nose. A novel threshold criterion is developed using the fuzzy membership values and is used to evaluate whether the electronic nose response is an outlier or a true data sample. The method is tested on two experimentally generated electronic nose datasets using different types of sensor arrays. As the experimental datasets does not contain outliers, a mathematical outlier generation model is developed for outlier generation following the inherent properties of outliers. The proposed method effectively discriminates among the true data samples and the outliers for both the electronic nose datasets. |
| Databáze: | OpenAIRE |
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