Name
Abstract | ||
|---|---|---|
In many practical situations, we monitor a system by continuously measuring the corresponding quantities, to make sure that any abnormal deviation is detected as early as possible. Often, we do not have readily available algorithms to detect abnormality, so we need to use machine learning techniques. For these techniques to be efficient, we first need to compress the data. One of the most successful methods of data compression is the technique of Symbolic Aggregate approXimation (SAX); see, e.g., [10]. While this technique is motivated by measurement uncertainty, it does not explicitly take this uncertainty into account. In this paper, we show that we can further improve upon this techniques if we explicitly take measurement uncertainty into account. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/NAFIPS-WConSC.2015.7284164 | 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC) |
Keywords | Field | DocType |
symbolic aggregate approximation,SAX,interval uncertainty,abnormal deviation,machine learning technique,data compression,measurement uncertainty | Symbolic aggregate approximation,Computer science,Measurement uncertainty,Sensitivity analysis,Uncertainty analysis,Artificial intelligence,Data compression,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chrysostomos D. Stylios | 1 | 649 | 52.33 |
| Vladik Kreinovich | 2 | 1091 | 281.07 |