Data Mining Using Unguided Symbolic Regression on a Blast Furnace Dataset

Kommenda, Michael; Kronberger, Gabriel; Feilmayr, Christoph; Affenzeller, Michael

doi:10.1007/978-3-642-20525-5_28

Cited by 4 publications

(3 citation statements)

References 7 publications

(7 reference statements)

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“…One modeling approach for the analysis of complex systems are variable interaction networks [8] -directed graphs representing system variables as nodes and their impact on others as weighted edges. Primarily, variable interaction networks have been employed to gain a better understanding of the interdependencies within a modeled system [4]. In this work however, we utilize them to detect changing system behavior -so-called concept drifts [3] -online.…”

Section: Motivationmentioning

confidence: 99%

Concept Drift Detection with Variable Interaction Networks

Zenisek

Kronberger

Wolfartsberger

et al. 2020

Computer Aided Systems Theory – EUROCAST 2019

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The current development of today's production industry towards seamless sensor-based monitoring is paving the way for concepts such as Predictive Maintenance. By this means, the condition of plants and products in future production lines will be continuously analyzed with the objective to predict any kind of breakdown and trigger preventing actions proactively. Such ambitious predictions are commonly performed with support of machine learning algorithms. In this work, we utilize these algorithms to model complex systems, such as production plants, by focussing on their variable interactions. The core of this contribution is a sliding window based algorithm, designed to detect changes of the identified interactions, which might indicate beginning malfunctions in the context of a monitored production plant. Besides a detailed description of the algorithm, we present results from experiments with a synthetic dynamical system, simulating stable and drifting system behavior.

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Section: Motivationmentioning

confidence: 99%

Concept Drift Detection with Variable Interaction Networks

Zenisek

Kronberger

Wolfartsberger

et al. 2020

Computer Aided Systems Theory – EUROCAST 2019

Self Cite

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“…According, to the authors [12] this paper revealed a data mining approach for variable selection and knowledge extraction from the dataset. The approach is based on unguided symbolic regression (every variable present in the dataset is treated as the target variable in multiple regression runs) and a novel variable relevance metric for genetic programming.…”

Section: Reviewmentioning

confidence: 99%

A Survey on Data Mining in Steel Industries

Umeshini¹,

P²

2017

IJCSES

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“…Kommenda et al (2011) used an unguided symbolic regression approach for variable selection and knowledge extraction from BF dataset. In this work, Genetic Programming (GP) (Koza 1992) was executed multiple times for reducing the stochastic effects and for identifying important variables through a variable interaction network.…”

Section: Figure 2: a Diagram Of The Blast Furnace In The Iron-making mentioning

confidence: 99%

An Event Group Based Classification Framework for Multi-variate Sequential Data

Sun

Stirling

2017

AJIS

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Decision tree algorithms were not traditionally considered for sequential data classification, mostly because feature generation needs to be integrated with the modelling procedure in order to avoid a localisation problem. This paper presents an Event Group Based Classification (EGBC) framework that utilises an X-of-N (XoN) decision tree algorithm to avoid the feature generation issue during the classification on sequential data. In this method, features are generated independently based on the characteristics of the sequential data. Subsequently an XoN decision tree is utilised to select and aggregate useful features from various temporal and other dimensions (as event groups) for optimised classification. This leads the EGBC framework to be adaptive to sequential data of differing dimensions, robust to missing data and accommodating to either numeric or nominal data types. The comparatively improved outcomes from applying this method are demonstrated on two distinct areas -a text based language identification task, as well as a honeybee dance behaviour classification problem. A further motivating industrial problem -hot metal temperature prediction, is further considered with the EGBC framework in order to address significant real-world demands.

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Data Mining Using Unguided Symbolic Regression on a Blast Furnace Dataset

Cited by 4 publications

References 7 publications

Concept Drift Detection with Variable Interaction Networks

Concept Drift Detection with Variable Interaction Networks

A Survey on Data Mining in Steel Industries

An Event Group Based Classification Framework for Multi-variate Sequential Data

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