Knowledge mining with genetic programming methods for variable selection in flavor design

Vladislavleva, Katya; Veeramachaneni, Kalyan; Burland, Matt; Parcon, Jason; O’Reilly, Una-May

doi:10.1145/1830483.1830651

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…In this section, a novel ML technique [genetic programming (GP)-based symbolic regression method [34]] is proposed to model the perceived quality, as an alternative to modeling the different complex properties of the HVS. As the name suggests, this method is applied to model the MOS score by means of a regression approach (not classification).…”

Section: Gp-based Symbolic Regressionmentioning

confidence: 99%

Constructing a No-Reference H.264/AVC Bitstream-Based Video Quality Metric Using Genetic Programming-Based Symbolic Regression

Staelens

Deschrijver

Vladislavleva³

et al. 2013

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel noreference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream.Index Terms-H.264/AVC, high definition, no-reference, objective video quality metric, quality of experience (QoE).

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Section: Gp-based Symbolic Regressionmentioning

confidence: 99%

Constructing a No-Reference H.264/AVC Bitstream-Based Video Quality Metric Using Genetic Programming-Based Symbolic Regression

Staelens

Deschrijver

Vladislavleva³

et al. 2013

IEEE Trans. Circuits Syst. Video Technol.

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“…A number of different variable relevance metrics for symbolic regression have been proposed in the literature [12]. In this contribution a simple frequencybased variable relevance metric is proposed, that is based on the number of variable references in all solution candidates visited in a GP run.…”

Section: Variable Relevance Metrics For Symbolic Regressionmentioning

confidence: 99%

Macro-economic Time Series Modeling and Interaction Networks

Kronberger¹,

Fink²,

Kommenda³

et al. 2011

Applications of Evolutionary Computation

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Abstract. Macro-economic models describe the dynamics of economic quantities. The estimations and forecasts produced by such models play a substantial role for financial and political decisions. In this contribution we describe an approach based on genetic programming and symbolic regression to identify variable interactions in large datasets. In the proposed approach multiple symbolic regression runs are executed for each variable of the dataset to find potentially interesting models. The result is a variable interaction network that describes which variables are most relevant for the approximation of each variable of the dataset. This approach is applied to a macro-economic dataset with monthly observations of important economic indicators in order to identify potentially interesting dependencies of these indicators. The resulting interaction network of macro-economic indicators is briefly discussed and two of the identified models are presented in detail. The two models approximate the help wanted index and the CPI inflation in the US.

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“…In [10] two different definitions of variable relevance are proposed. The presence weighted variable importance calculates the relative number of models, identified and manually selected by one or multiple ParetoGP [8] runs, which reference this variable.…”

Section: Variable Relevance Metrics For Gpmentioning

confidence: 99%

Data Mining Using Unguided Symbolic Regression on a Blast Furnace Dataset

Kommenda

Kronberger

Feilmayr

et al. 2011

Applications of Evolutionary Computation

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Abstract. In this paper a data mining approach for variable selection and knowledge extraction from datasets is presented. 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. The relevance of each input variable is calculated and a model approximating the target variable is created. The genetic programming configurations with different target variables are executed multiple times to reduce stochastic effects and the aggregated results are displayed as a variable interaction network. This interaction network highlights important system components and implicit relations between the variables. The whole approach is tested on a blast furnace dataset, because of the complexity of the blast furnace and the many interrelations between the variables. Finally the achieved results are discussed with respect to existing knowledge about the blast furnace process.

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Knowledge mining with genetic programming methods for variable selection in flavor design

Cited by 11 publications

References 12 publications

Constructing a No-Reference H.264/AVC Bitstream-Based Video Quality Metric Using Genetic Programming-Based Symbolic Regression

Constructing a No-Reference H.264/AVC Bitstream-Based Video Quality Metric Using Genetic Programming-Based Symbolic Regression

Macro-economic Time Series Modeling and Interaction Networks

Data Mining Using Unguided Symbolic Regression on a Blast Furnace Dataset

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