Knowledge in Imperfect Data

Kochański, Andrzej; Perzyk, M.; Klebczyk, Marta

doi:10.5772/37714

Cited by 6 publications

(7 citation statements)

References 33 publications

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“…The method for determining the missing values is presented in [2]. A significant problem related to the data published in the articles was also the fact that the hardness measurement method was presented in various scales, i.e.…”

Section: Modelmentioning

confidence: 99%

“…The problem of "missing data" has been discussed in [2]. Based on this diagram, assumptions were made for the own method of completing the missing data:…”

Section: Completing the Missing Datamentioning

confidence: 99%

“…Already in one of the first scientific papers on this topic [1] models in which more than one property is predicted experience problems with missing data for one of them. This problem, together with an example of a solution, is presented in the paper [2]. To fill in the missing values, one should find a subset of samples in which, for the dimension with missing data, there is a highly correlated other dimension, on the basis of which it is possible to determine the missing value, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…(1,2,3,4,5) have been created, which divide the values into 5 sets of similar size, by determining the range of values included in a given set. The ranges of values for each class for each of the mechanical properties were as follows: UST: 1 − [UST min , 960), 2 − [960, 1045), 3 − [1045, 1127), 4 − [1127, 1264), 5 − [1264, UST max ], YS: 1 − [Rp 02min , 696), 2 − [696, 813), 3 − [813, 900), 4 − [900, 1100), 5 − [1100, YS max ], HB: 1 − [HB min , 286),2 − [286,325), 4 − [370,415), 5 − [415,HB max ], A: 1 − [A min , 2.7), 2 − [2.7,4.5),3 − [4.5,6.5), 4 − [6.5,8.4), 5 − [8.4,A], K: 1 − [K min ,52), 2 − [52,75), 3 − [75,90), 4 − [90,110), 5 − [110,K max ].…”

mentioning

confidence: 99%

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Selection of casting production parameters with the use of machine learning and data supplementation methods in order to obtain products with the assumed parameters

Wilk-Kołodziejczyk

Pirowski

Bitka

et al. 2023

Archiv.Civ.Mech.Eng

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The main purpose of the research, presented in this publication, was to develop methodology for the construction of predictive models which allow the selection of material production parameters for the material-technological conversion process. The development of prototype modules based on information-decision system allows an initial assessment of the level of feasibility of undertaking this type of operation. Algorithms 1, 2, 3 presented in the article were used to complete the missing data. The result of the algorithm enabled the creation of a data table that specifies the operation of the predictive models indicated in chapter 3 of this article. Entire work is presented with regard to the background of the ADI cast iron production process to locate the requirement where to apply the developed methods in the field of predictive algorithms and data completion algorithms. On the basis of developed methods and predictive algorithms, trial castings were operated.

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Section: Modelmentioning

confidence: 99%

“…The problem of "missing data" has been discussed in [2]. Based on this diagram, assumptions were made for the own method of completing the missing data:…”

Section: Completing the Missing Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Selection of casting production parameters with the use of machine learning and data supplementation methods in order to obtain products with the assumed parameters

Wilk-Kołodziejczyk

Pirowski

Bitka

et al. 2023

Archiv.Civ.Mech.Eng

View full text Add to dashboard Cite

show abstract

“…Preprocessing consists of data cleaning, data integration, data reduction and data transformation [17] and includes several techniques [1,27,29]. The majority of the preprocessing papers have studied single data problems and competing corrections to them [43].…”

Section: Related Researchmentioning

confidence: 99%

Data Quality Visualization for Preprocessing

Vattulainen

2016

Advances in Data Mining. Applications and Theoretical Aspects

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Abstract. Preprocessing is often the most time-consuming phase in data analysis and interdependent data quality issues a cause of suboptimal modelling results. The design problem addressed in this paper is: what kind of framework can support visualization of data quality issue interdependencies for faster and more effective preprocessing? An object framework was designed that uses constructed features as a basis of visualizations. Six real datasets from business performance measurement system domain were acquired to demonstrate the implementation. The framework was found to be a viable preprocessing analysis supplement to both industry practice of exploratory data analysis and research benchmark of preprocessing combinations.

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Data and Modeling in Industrial Manufacturing

Grzegorzewski

Kochański

2018

Studies in Systems, Decision and Control

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Knowledge in Imperfect Data

Cited by 6 publications

References 33 publications

Selection of casting production parameters with the use of machine learning and data supplementation methods in order to obtain products with the assumed parameters

Selection of casting production parameters with the use of machine learning and data supplementation methods in order to obtain products with the assumed parameters

Data Quality Visualization for Preprocessing

Data and Modeling in Industrial Manufacturing

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