Application of a hybrid method of machine learning for description and on-line estimation of methane hazard in mine workings

Sikora, Marek; Krzystanek, Z.; Bojko, B.; Śpiechowicz, K.

doi:10.1134/s1062739147040125

Cited by 17 publications

(12 citation statements)

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“…Detailed analysis of results for methane concentration forecasting in various mine excavations made by the M5 algorithms is presented by Sikora et al (2011). In the case of the second data set, application of the ARIMA methodology did not give better results.…”

Section: Discussionmentioning

confidence: 99%

“…Training and testing data sets contained 679 and 286 examples, respectively. A detailed description of that application and the whole infrastructure of prediction system are presented by Sikora and Sikora (2006) as well as Sikora et al (2011). However, in the papers no approach exploiting the k-nn algorithm is applied.…”

Section: Discussionmentioning

confidence: 99%

“…It is used by the forecasting module that is a component of a methane risks monitoring system (Sikora et al, 2011). Our further research will focus on full automation of the process of the ARIMA model constructing and shortening the duration of searching values of the k-opty parameter.…”

Section: Discussionmentioning

confidence: 99%

“…The first consists in introducing into a set of variables based on which M5 makes rule induction a new meta-variable. (Sikora and Wróbel, 2010;Sikora and Krzykawski, 2005;Sikora et al, 2011) shows that using too many delays leads to obtaining models unduly matched to training data, which are burdened with a big error on new unknown data. This observation is the second reason for introducing the meta-variable represented by values returned by the autoregressive or ARIMA models.…”

Section: Combination Of Time Series Prediction Techniques and The K-nmentioning

confidence: 99%

“…Therefore, new forecasting methods based on historical data collected in databases of monitoring systems are still being worked out. In the papers by Dixon (1992), Gale et al (2001), Kabiesz (2005), Sikora and Wróbel (2010), Sikora and Sikora (2006), or Sikora et al (2011), propositions of application of machine learning methods to improve the forecast of seismic and methane hazards are presented.…”

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confidence: 99%

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Improving prediction models applied in systems monitoring natural hazards and machinery

Sikora

2012

International Journal of Applied Mathematics and Computer Science

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A method of combining three analytic techniques including regression rule induction, the k-nearest neighbors method and time series forecasting by means of the ARIMA methodology is presented. A decrease in the forecasting error while solving problems that concern natural hazards and machinery monitoring in coal mines was the main objective of the combined application of these techniques. The M5 algorithm was applied as a basic method of developing prediction models. In spite of an intensive development of regression rule induction algorithms and fuzzy-neural systems, the M5 algorithm is still characterized by the generalization ability and unbeatable time of data model creation competitive with other systems. In the paper, two solutions designed to decrease the mean square error of the obtained rules are presented. One consists in introducing into a set of conditional variables the so-called meta-variable (an analogy to constructive induction) whose values are determined by an autoregressive or the ARIMA model. The other shows that limitation of a data set on which the M5 algorithm operates by the k-nearest neighbor method can also lead to error decreasing. Moreover, three application examples of the presented solutions for data collected by systems of natural hazards and machinery monitoring in coal mines are described. In Appendix, results of several benchmark data sets analyses are given as a supplement of the presented results.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Combination Of Time Series Prediction Techniques and The K-nmentioning

confidence: 99%

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confidence: 99%

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