Mapping imbalanced soil classes using Markov chain random fields models treated with data resampling technique

Sharififar, Amin; Sarmadian, Fereydoon; Minasny, Budiman

doi:10.1016/j.compag.2019.03.006

Cited by 28 publications

(14 citation statements)

References 23 publications

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“…When the predicted soil classes compared with actual soil profiles was overlaid on the maps (Figure ), we could conclude that the RF model trained on the SMOTE resampled data model was much more successful in DSM. These results are in line with the works of Sharififar, Sarmadian, Malone, and Minasny () and Sharififar, Sarmadian, and Minasny (), who indicated balancing the soil dataset helped overcome the issue of modelling imbalanced soil data by improving the predictive models’ results.…”

Section: Resultssupporting

confidence: 89%

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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: Resultssupporting

confidence: 89%

“…Contrary to our findings, Sharififar, Sarmadian, Malone, and Minasny () indicated a significant improvement in ML learning when they made balanced soil data using ROS. They also pointed out that balancing the soil classes led to a notable decrease in uncertainty of ML algorithms (Sharififar, Sarmadian, & Minasny, ).…”

Section: Resultsmentioning

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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“…erefore, grades alone cannot truly reflect the quality of teaching at this stage. e evaluation method established by the Markov chain can make up for this defect [10][11][12]. e specific method is as follows.…”

Section: E Relationship Between Markov Chain and Hybridmentioning

confidence: 99%

[Retracted] Algorithm of Classroom Teaching Quality Evaluation Based on Markov Chain

Yuan

2021

Complexity

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The Markov chain model teaching evaluation method is a quantitative analysis method based on probability theory and stochastic process theory, which establishes a stochastic mathematical model to analyse the quantitative relationship in the change and development process of real activities. Applying it to achieve a more comprehensive, reasonable, and effective evaluation of the classroom teaching quality of college teachers is of positive significance for promoting the continuous improvement of the teaching level of teachers and the teaching quality of schools. Therefore, after an in-depth study of Markov chain algorithm theory, this research proposes an improved Markov chain hybrid teaching quality evaluation model and designs comparative experiments and applies it to the hybrid teaching quality evaluation system of universities, designs a corresponding hybrid teaching quality evaluation model, and finally verifies its effectiveness through experiments. The mathematical model of mixed classroom teaching quality evaluation given in this research focuses on the development and change of the teaching process. For the teaching process that is closely related to the causality of teaching quality, the model established in this paper is more objective and reasonable for evaluating the quality of teaching.

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“…Learning from imbalanced data is an important and hot topic in machine learning, as it has been widely applied to diagnose and classify diseases [1,2], detect software defects [3,4], analyze biology and pharmacology data [5,6], evaluate credit risk [7], predict actionable revenue change and bankruptcy [8,9], diagnose faults in the industrial procedure [10,11], classify soil types [12,13], and even predict crash injury severity [14] or analyze crime linkages [15]. Meanwhile, class imbalance learning (CIL) is also a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Probability Density Machine: A New Solution of Class Imbalance Learning

Cheng

Longfei

et al. 2021

Scientific Programming

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Class imbalance learning (CIL) is an important branch of machine learning as, in general, it is difficult for classification models to learn from imbalanced data; meanwhile, skewed data distribution frequently exists in various real-world applications. In this paper, we introduce a novel solution of CIL called Probability Density Machine (PDM). First, in the context of Gaussian Naive Bayes (GNB) predictive model, we analyze the reason why imbalanced data distribution makes the performance of predictive model decline in theory and draw a conclusion regarding the impact of class imbalance that is only associated with the prior probability, but does not relate to the conditional probability of training data. Then, in such context, we show the rationality of several traditional CIL techniques. Furthermore, we indicate the drawback of combining GNB with these traditional CIL techniques. Next, profiting from the idea of K-nearest neighbors probability density estimation (KNN-PDE), we propose the PDM which is an improved GNB-based CIL algorithm. Finally, we conduct experiments on lots of class imbalance data sets, and the proposed PDM algorithm shows the promising results.

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Mapping imbalanced soil classes using Markov chain random fields models treated with data resampling technique

Cited by 28 publications

References 23 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

[Retracted] Algorithm of Classroom Teaching Quality Evaluation Based on Markov Chain

Probability Density Machine: A New Solution of Class Imbalance Learning

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