Special Issue "Applied Machine Learning III"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2023 | Viewed by 1290

Special Issue Editor

Department of Automatic Control, Electrical Engineering and Optoelectronics, Faculty of Electrical Engineering, Częstochowa University of Technology, Al. Armii Krajowej 17, 42-200 Częstochowa, Poland
Interests: machine learning; evolutionary computation; artificial intelligence; pattern recognition; data mining and applications in forecasting, classification, regression, and optimization problems
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Special Issue Information

Dear Colleagues,

Machine learning (ML) is one of the most exciting fields of computing today. In recent decades, ML has become an entrenched part of everyday life and has been successfully used to solve practical problems. The application area of machine learning is very broad, including engineering, industry, business, finance, medicine, and many other domains. ML covers a wide range of learning algorithms, including classic ones such as linear regression, k-nearest neighbors, or decision trees, through support vector machines and neural networks, to newly developed algorithms such as deep learning and boosted tree models. In practice, it is quite challenging to properly determine the appropriate architecture and parameters of ML models so that the resulting learner model can achieve sound performance for both learning and generalization. Practical applications of ML bring additional challenges such as dealing with big, missing, distorted, and uncertain data. In addition, interpretability is a paramount quality that ML methods should aim to achieve if they are to be applied in practice. Interpretability allows us to understand ML model operation and raises confidence in its results.

This Special Issue focuses on applications of ML models in a diverse range of fields and problems. Application papers are expected to report substantive results on a wide range of learning methods, discussing conceptualization of a problem, data representation, feature engineering, ML models, critical comparisons with existing techniques, and the interpretation of results. Specific attention will be given to recently developed ML methods, such as deep learning and boosted tree models.

Dr. Grzegorz Dudek
Guest Editor

Manuscript Submission Information

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Published Papers (1 paper)

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Analysis of Preprocessing Techniques for Missing Data in the Prediction of Sunflower Yield in Response to the Effects of Climate Change
Appl. Sci. 2023, 13(13), 7415; https://doi.org/10.3390/app13137415 - 22 Jun 2023
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Machine learning is often used to predict crop yield based on the sowing date and weather parameters in non-irrigated crops. In the context of climate change, regression algorithms can help identify correlations and plan agricultural activities to maximise production. In the case of [...] Read more.
Machine learning is often used to predict crop yield based on the sowing date and weather parameters in non-irrigated crops. In the context of climate change, regression algorithms can help identify correlations and plan agricultural activities to maximise production. In the case of sunflower crops, we identified datasets that are not very large and have many missing values, generating a low-performance regression model. In this paper, our aim is to study and compare several approaches for missing-value imputation in order to improve our regression model. In our experiments, we compare nine imputation methods, using mean values, similar values, interpolation (linear, spline, pad), and prediction (linear regression, random forest, extreme gradient boosting regressor, and histogram gradient boosting regression). We also employ four unsupervised outlier removal algorithms and their influence on the regression model: isolation forest, minimum covariance determinant, local outlier factor and OneClass-SVM. After preprocessing, the obtained datasets are used to build regression models using the extreme gradient boosting regressor and histogram gradient boosting regression, and their performance is compared. The evaluation of the models shows an increased R2 from 0.723 when removing instances with missing data, to 0.938 for imputation using Random Forest prediction and OneClass-SVM-based outlier removal. Full article
(This article belongs to the Special Issue Applied Machine Learning III)
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