Using Boosted k-Nearest Neighbour Algorithm for Numerical Forecasting of Dangerous Convective Phenomena

Stankova, Elena N.; Khvatkov, Evgeny V.

doi:10.1007/978-3-030-24305-0_61

Cited by 5 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the paper we continue to examine the effectiveness of using various machine learning methods for forecasting dangerous convective phenomena. Table 3 illustrates the results obtained both in the previous works [ 4 , 5 , 20 ] and present paper. We have considered five machine learning methods: Support Vector Machine (SVM), Logistic Regression, Ridge Regression, boosted k-nearest neighbour algorithm and neural networks.…”

Section: Discussionmentioning

confidence: 69%

“…As it can be seen from the Table 3 the best results are achieved using Boosted k-nearest neighbour algorithm [ 20 ], the worst results were achieved with the neural networks algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…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%

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

Self Cite

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: Discussionmentioning

confidence: 69%

“…As it can be seen from the Table 3 the best results are achieved using Boosted k-nearest neighbour algorithm [ 20 ], the worst results were achieved with the neural networks algorithms.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

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

Self Cite

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

“…analyzes the complex and non-linear problems [10], [11]. In comparison, the Adaptive Boosting algorithm is one variant of several boosting algorithms that can change the weak classification model into a strong one [12].…”

mentioning

confidence: 99%

Comparative Analysis Performance of K-Nearest Neighbor Algorithm and Adaptive Boosting on the Prediction of Non-Cash Food Aid Recipients

Yustikasari

Mubarok

Rianto

2022

SJI

View full text Add to dashboard Cite

Purpose: The implementation of this manual system is considered less accurate in obtaining the results of social assistance recipients. From these problems to overcome this problem, systematic calculations are needed. In processing data, a model is needed that can explain the data with its application, so a machine learning model is made that can help process the data.Methods: This study's classification of non-cash food social assistance receipts uses the K-Nearest Neighbor and Adaptive Boosting algorithms. This study will compare the performance of the two algorithms.Result: The results obtained for Adaptive Boosting are the best classification results with a maximum accuracy of 100% and produce a high AUC value of 1.0. In comparison, the ROC curve for the K-Nearest Neighbor algorithm produces an accuracy of 96% with an AUC value of 0.94.Novelty: ROC curves in the two algorithms are good classification results because the two graphs cross above the diagonal line and produce an AUC value included in the Excellent classification.

show abstract

Using Boosted k-Nearest Neighbour Algorithm for Numerical Forecasting of Dangerous Convective Phenomena

Cited by 5 publications

References 19 publications

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

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

Domain-Oriented Multilevel Ontology for Adaptive Data Processing

Comparative Analysis Performance of K-Nearest Neighbor Algorithm and Adaptive Boosting on the Prediction of Non-Cash Food Aid Recipients

Contact Info

Product

Resources

About