Imbalance Data Classification via Neural-Like Structures of Geometric Transformations Model: Local and Global Approaches

Tkachenko, Roman; Doroshenko, Anastasiya; Izonin, Ivan; Tsymbal, Yurii; Havrysh, Bohdana

doi:10.1007/978-3-319-91008-6_12

Cited by 23 publications

(17 citation statements)

References 15 publications

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“…Although many of the resampling techniques have been proposed and successfully applied to cope with problems of imbalanced data in mathematics and informatics (Tkachenko, Doroshenko, Izonin, Tsymbal, & Havrysh, ), to the best of our knowledge, these techniques have not been widely tested in DSM studies for large areas and at a national scale. There are only a few studies related to different balanced sampling techniques for soil science, mostly limited in sampling size and techniques.…”

Section: Introductionmentioning

confidence: 99%

Synthetic resampling strategies and machine learning for digital soil mapping in Iran

Taghizadeh‐Mehrjardi¹,

Schmidt

Eftekhari

et al. 2020

European J Soil Science

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Most common machine learning (ML) algorithms usually work well on balanced training sets, that is, datasets in which all classes are approximately represented equally. Otherwise, the accuracy estimates may be unreliable and classes with only a few values are often misclassified or neglected. This is known as a class imbalance problem in machine learning and datasets that do not meet this criterion are referred to as imbalanced data. Most datasets of soil classes are, therefore, imbalanced data. One of our main objectives is to compare eight resampling strategies that have been developed to counteract the imbalanced data problem. We compared the performance of five of the most common ML algorithms with the resampling approaches.The highest increase in prediction accuracy was achieved with SMOTE (the synthetic minority oversampling technique). In comparison to the baseline prediction on the original dataset, we achieved an increase of about 10, 20 and 10% in the overall accuracy, kappa index and F-score, respectively. Regarding the ML approaches, random forest (RF) showed the best performance with an overall accuracy, kappa index and F-score of 66, 60 and 57%, respectively. Moreover, the combination of RF and SMOTE improved the accuracy of the individual soil classes, compared to RF trained on the original dataset and allowed better prediction of soil classes with a low number of samples in the corresponding soil profile database, in our case for Chernozems. Our results show that balancing existing soil legacy data using synthetic sampling strategies can significantly improve the prediction accuracy in digital soil mapping (DSM). Highlights• Spatial distribution of soil classes in Iran can be predicted using machine learning (ML) algorithms. • The synthetic minority oversampling technique overcomes the drawback of imbalanced and highly biased soil legacy data. • When combining a random forest model with synthetic sampling strategies the prediction accuracy of the soil model improves significantly.

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Section: Introductionmentioning

confidence: 99%

Synthetic resampling strategies and machine learning for digital soil mapping in Iran

Taghizadeh‐Mehrjardi¹,

Schmidt

Eftekhari

et al. 2020

European J Soil Science

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“…Further research can be conducted in the direction of applying new splitting criteria for the developed method. In order to improve the accuracy of the classification and clustering, regression, and prediction tasks for the solution of various problems in material science, it is also planned to use neural-like structures of the Successive Geometric Transformations Model [11].…”

Section: Discussion Of the Developed Methods Resultsmentioning

confidence: 99%

“…of artificial intelligence [9,10] increases the efficiency of the procedure for developing or designing new materials. Traditional approaches allow obtaining all necessary information about the material properties [7], and the usage of powerful [11], modern ML algorithms [12][13] makes this process easier, shorter and cheaper. This is due to solving prediction and regression tasks, classification or clustering on a small experimental data sample and extrapolation of the results to the new material.…”

Section: на основі експериментально встановлених даних щодо параметріmentioning

confidence: 99%

Development of machine learning method of titanium alloy properties identification in additive technologies

Tkachenko¹,

Duriagina²,

Lemishka³

et al. 2018

EEJET

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“…Tepla et al develop a classification method based on the application of multiclass logistic regression for the design of biocompatible materials in medical products in order to reduce the probability of incorrect alloy identification. R. Tkachenko et al [19] compare the results of solving data classification problems using the most common classification methods and describe a new classification method based on neural-like structures of the geometric transformation model.…”

Section: Related Workmentioning

confidence: 99%

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

et al. 2020

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Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.

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Imbalance Data Classification via Neural-Like Structures of Geometric Transformations Model: Local and Global Approaches

Cited by 23 publications

References 15 publications

Synthetic resampling strategies and machine learning for digital soil mapping in Iran

Synthetic resampling strategies and machine learning for digital soil mapping in Iran

Development of machine learning method of titanium alloy properties identification in additive technologies

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

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