Classification Method of Fictitious Enterprises Based on Gaussian Naive Bayes

Krysovatyy, Andriy; Lipyanina-Goncharenko, Hrystyna; Desyatnyuk, Oksana; Sachenko, Svitlana; Lukasevych-Krutnyk, Iryna; Butrin-Boka, Natalia

doi:10.1109/csit52700.2021.9648584

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…На основі цих даних можна проводити передбачення груп за допомогою методів машинного навчання. Для цього використано метод Logistic Regression [9] та Naive Bayes [2], адже ці методи мають найпростішу логіку кваліфікації та хороші результати оцінки моделювання. large sum (RFMScore = 114) і Weak tender organizers (RFMScore = 111).…”

Section: методunclassified

Method for Forming Training Samples for Data Arrays Based on Machine Learning

Lipianina-Honcharenko

2024

NRPCOMP

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The study introduces an innovative methodology for crafting training samples through the integration of machine learning techniques. This method encompasses a fusion of RFM (Recency, Frequency, Monetary) analysis and cluster analysis, offering a comprehensive approach to sample formation. The application of this approach is demonstrated on a dataset derived from concluded tender agreements by participants in Ukraine, sourced from the ProZorro Sales platform. The compiled dataset encompasses an impressive volume, encompassing a total of 92,638 auctions, which further breaks down into 29,164 distinct auctions and an assemblage of 39,747 unique organizers.The utilization of RFM analysis within this framework yields the categorization of the dataset into distinct groups, each characterized by its own distinct attributes. These groupings include designations such as “The Best Organizers of Tenders,” “Loyal Organizers of Tenders,” “Large Consumers,” “Tenders Held Infrequently but with Substantial Sums,” and “Weak Tender Organizers.” Following the RFM analysis, the K-means clustering methodology is implemented, resulting in the division of the data into five clusters, each contributing to a nuanced differentiation of diverse organizer profiles.Intriguingly, a comparative analysis involving RTF (Relative Total Frequency) scores and the K-means groupings reveals congruence between clusters representing organizers who actively orchestrate numerous tenders with significant monetary value, as well as clusters characterized by minimal tender activity with less substantial monetary implications. To validate the efficacy of the proposed method, rigorous testing is conducted employing Logistic Regression and Naive Bayes algorithms. Encouragingly, the results consistently showcase impressive accuracy for both methods, highlighting their robustness.An outlook towards future research endeavors suggests a promising avenue of developing an automated system for the selection of tender organizers, underpinned by machine learning principles. Such a system would undoubtedly revolutionize the optimization of participation strategies within the domain of tender processes, fostering efficiency and accuracy in decision-making.

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Section: методunclassified

Method for Forming Training Samples for Data Arrays Based on Machine Learning

Lipianina-Honcharenko

2024

NRPCOMP

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“…As the LGBoost method confirmed the best accuracy (0.99%) we use it for further modeling the test data. After modeling on the test sample, a report was generated in the form of a Confusion Matrix [35] (Figure 4). As it can be seen from Figure 4 Moreover, the potential accident levels I and V are better classified with the accuracy of 0.97 and 0.94, respectively (see Figure 4).…”

Section: Classification Based On Textual Datamentioning

confidence: 99%

Intelligent Method for Classifying the Level of Anthropogenic Disasters

Lipianina-Honcharenko,

Wolff,

Sachenko

et al. 2023

BDCC

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Anthropogenic disasters pose a challenge to management in the modern world. At the same time, it is important to have accurate and timely information to assess the level of danger and take appropriate measures to eliminate disasters. Therefore, the purpose of the paper is to develop an effective method for assessing the level of anthropogenic disasters based on information from witnesses to the event. For this purpose, a conceptual model for assessing the consequences of anthropogenic disasters is proposed, the main components of which are the following ones: the analysis of collected data, modeling and assessment of their consequences. The main characteristics of the intelligent method for classifying the level of anthropogenic disasters are considered, in particular, exploratory data analysis using the EDA method, classification based on textual data using SMOTE, and data classification by the ensemble method of machine learning using boosting. The experimental results confirmed that for textual data, the best classification is at level V and level I with an error of 0.97 and 0.94, respectively, and the average error estimate is 0.68. For quantitative data, the classification accuracy of Potential Accident Level relative to Industry Sector is 77%, and the f1-score is 0.88, which indicates a fairly high accuracy of the model. The architecture of a mobile application for classifying the level of anthropogenic disasters has been developed, which reduces the time required to assess consequences of danger in the region. In addition, the proposed approach ensures interaction with dynamic and uncertain environments, which makes it an effective tool for classifying.

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