Modelling economic choice under radical uncertainty: machine learning approaches

Gerunov, Anton

doi:10.1504/ijbidm.2019.096794

Cited by 4 publications

(3 citation statements)

References 29 publications

(30 reference statements)

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“…Data mining is a series of methods and techniques used to extract knowledge from data. The extraction process is very complicated, mainly due to the large-scale, irregular, and noisy characteristics of the data [ 21 ].…”

Section: Data Mining Technology and Economic Intelligence Systemsmentioning

confidence: 99%

Research and Application of the Data Mining Technology in Economic Intelligence System

Chang

2022

Computational Intelligence and Neuroscience

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In the context of the rapid development of the modern economy, information is particularly important in the economic field, and information determines the decision-making of enterprises. Therefore, how to quickly dig out information that is beneficial to the enterprise has become a crucial issue. This topic applies data mining technology to economic intelligent systems and obtains the data object model of economic intelligent systems through the integration of information. This article analyzes the interrelationship between its objects on this basis and uses data mining-related methods to mine it. The establishment of economic intelligence systems not only involves the establishment of mathematical models of economic systems, but also includes research on the algorithms applied to them. How to apply an algorithm to quickly and accurately extract the required economic intelligence domain information from the potential information in the database, or to provide a method to find the best solution, involves the use of association rules and classification prediction methods. The application of data mining algorithms can be used to study the application of economic intelligence systems. This paper develops and designs an economic intelligence information database and realizes the economic intelligence system on this basis, and realizes the research results. Finally, this paper has tested the dataset, and the results show that the classification accuracy of this algorithm is 2.64% higher than that of the ID3 algorithm.

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Section: Data Mining Technology and Economic Intelligence Systemsmentioning

confidence: 99%

Research and Application of the Data Mining Technology in Economic Intelligence System

Chang

2022

Computational Intelligence and Neuroscience

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“…Among the most popular regression algorithms in the field of machine learning that are applicable to this task are the neural network, the kNN algorithm, the random forest, and the support vector machine (for another application see e.g. Gerunov, 2019). Their predictive accuracy is compared with that of the linear regression in Table 3.…”

Section: Figure No 4 Relative Variable Importance In a Linear Multipl...mentioning

confidence: 99%

Machine Learning Algorithms for Forecasting Asset Prices: An Application to the Housing Market

Gerunov

2020

Economics and Management

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This article investigates the application of advanced machine learning algorithms for forecasting housing prices. To this end we leverage a dataset of 414 observations of housing deals in Taipei and model it with both traditional econometric and novel machine learning algorithms. An exhaustive search among 107 alternative methods is conducted and their forecast accuracy is reported in detail. Using the root mean squared error (RMSE) as a benchmark metric, we find that implementations of the random forest family have superior performance, far surpassing that of more traditional approaches such as the multiple linear regression. The results have utility for both academics and practitioners and can be easily t ransferred to other forecasting problems in economics and business.

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“…Доброто представяне на случайната гора е видимо и в трите разгледани казуса, като този резултат не е необичаен (вж. Fernandez-Delgado et al, 2014;Gerunov, 2019).…”

Section: приложение при управление на кредитен рискunclassified

Classification algorithms for modeling economic choice

Gerunov¹

2020

etj

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The article shows how some novel machine learning algorithms can be applied to economic problems of discrete binary choice. An examination is made of three typical business tasks – classifying overdraft applications, credit risk management, and marketing segmentation. Both traditional econometric methods (logistic regression and linear discriminant analysis) as well as five more advanced machine learning algorithms (neural networks, k-nearest neighbours, naïve Bayes classifier, random forest, and support vector machine) have been used for modelling these tasks. For all the classification tasks, the random forest algorithm robustly registers improved forecasting accuracy over the more traditional approaches. This underlines the need to supplement the classical econometric toolbox with innovative methods, with the random forest, the support vector machine, and the neural network being prime candidates.

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Modelling economic choice under radical uncertainty: machine learning approaches

Cited by 4 publications

References 29 publications

Research and Application of the Data Mining Technology in Economic Intelligence System

Research and Application of the Data Mining Technology in Economic Intelligence System

Machine Learning Algorithms for Forecasting Asset Prices: An Application to the Housing Market

Classification algorithms for modeling economic choice

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