Explaining a Random Forest With the Difference of Two ARIMA Models in an Industrial Fault Detection Scenario
| Autor: | Anna-Christina Glock |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Basis (linear algebra)
Computer science 020206 networking & telecommunications Failure rate 02 engineering and technology Fault detection and isolation Random forest Moving average Statistics 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Autoregressive integrated moving average General Environmental Science Linear trend |
| Zdroj: | Procedia Computer Science. 180:476-481 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2021.01.360 |
| Popis: | In this paper a method is proposed to obtain explainability of the random forest model. Two Auto-Regressive Integrated Moving Average (ARIMA) models form the basis for this approach. The ARIMA models are used in a way similar to how local surrogate models are typically applied. The explanation of random forest’s prediction is derived from the numerical differences of the parameters of the ARIMA models. To demonstrate the feasibility of this idea, an experiment that implements this approach is conducted. The data used for this are similar to an accumulated bathtub curve representing failure rates in a production process. The results of the experiment show that the approach is able to identify a linear trend in some parts of the data, and therefore locally provide an explanation for the functional form of the underlying failure rate. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|