OLAP technology and machine learning as the tools for validation of the numerical models of convective clouds

Stankova, Elena N.; Balakshiy, Andrey V.; Petrov, Dmitry; Korkhov, Vladimir; Shorov, Andrey

doi:10.1504/ijbidm.2019.096793

Cited by 9 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use three approaches for feature selection. The first one, described in detail in [ 4 , 5 , 19 , 20 ] is used for the following machine learning algorithms: Support Vector Machine (SVM), Logistic Regression, Ridge Regression and boosted k-nearest neighbour algorithm.…”

Section: Algorithms For Data Formation and Preprocessingmentioning

confidence: 99%

“…The second approach is used in the works [ 4 , 5 ] to refine the results obtained with Support Vector Machine (SVM), Logistic Regression and Ridge Regression algorithms.…”

Section: Algorithms For Data Formation and Preprocessingmentioning

confidence: 99%

“…Forecast accuracy amounts to 97.7%, 98.6% and 98.1% for Support Vector Machine, Logistic Regression and Ridge Regression correspondingly while using the first approach of data formation and preprocessing [ 4 , 5 ]. Though looking very promising these results need to be checked and clarified.…”

Section: Forecast Accuracy Using Support Vector Machine (Svm) Logistmentioning

confidence: 99%

“…[ 1 – 3 ]. On the other hand, machine learning methods are used to verify models by establishing relationships between model forecasts and the actual meteorological situation [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the Effectiveness of Using Various Machine Learning Methods for Forecasting Dangerous Convective Phenomena

Stankova

Tokareva

Dyachenko

2020

Computational Science and Its Applications – ICCSA 2020

View full text Add to dashboard Cite

The paper considers the possibility of thunderstorm forecasting using only dynamical and microphysical parameters of the cloud, simulated by the 1.5D model with further processing by machine learning methods. The problem of feature selection is discussed in two aspects: selection of the optimal values of time and height when and where the output model data are fixed and selection of fixed set of the most representative cloud parameters (features) among all output cloud characteristics. Five machine learning methods are considered: Support Vector Machine (SVM), Logistic Regression, Ridge Regression, boosted k-nearest neighbour algorithm and neural networks. It is shown that forecast accuracy of all five methods reaches values exceeding 90%.

show abstract

Section: Algorithms For Data Formation and Preprocessingmentioning

confidence: 99%

“…The second approach is used in the works [ 4 , 5 ] to refine the results obtained with Support Vector Machine (SVM), Logistic Regression and Ridge Regression algorithms.…”

Section: Algorithms For Data Formation and Preprocessingmentioning

confidence: 99%

Section: Forecast Accuracy Using Support Vector Machine (Svm) Logistmentioning

confidence: 99%

“…[ 1 – 3 ]. On the other hand, machine learning methods are used to verify models by establishing relationships between model forecasts and the actual meteorological situation [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Effectiveness of Using Various Machine Learning Methods for Forecasting Dangerous Convective Phenomena

Stankova

Tokareva

Dyachenko

2020

Computational Science and Its Applications – ICCSA 2020

View full text Add to dashboard Cite

show abstract

“…Also, many IT specialists are ready to use the knowledge discovery process in various domains such as economic, meteorology, etc. [ 21 , 22 ]. But the diversity of algorithms and the clutter of data make the knowledge discovery process very unfriendly to many non-computer professional researchers.…”

Section: Introductionmentioning

confidence: 99%

Domain-Oriented Multilevel Ontology for Adaptive Data Processing

Tianxing

Stankova

Vodyaho

et al. 2020

Computational Science and Its Applications – ICCSA 2020

View full text Add to dashboard Cite

In the data mining domain, the diversity of algorithms and the clutter of data make the knowledge discovery process very unfriendly to many non-computer professional researchers. Meta-learning helps users to modify some aspects of this process to improve the performance of the resulting model. Semantic meta mining is the process of mining metadata about data mining algorithms based on expertise extracted from the knowledge base. The knowledge base is usually represented in the form of ontology. This article proposes a domain-oriented multi-level ontology (DoMO) through merging and improving existing data mining ontologies. It provides the restrictions of the dataset characteristics to help the domain experts describe data set in the form of ontology entities. According to the entities of the data characteristics in DoMO, the users can query the ontology to obtain the optimized data processing process. In this paper, we take the time series classification problem as an example to present the effectiveness of the proposed ontology.

show abstract