Fuzzy regression functions with a noise cluster and the impact of outliers on mainstream machine learning methods in the regression setting

Chakravarty, Srinivas; Demirhan, Haydar; Baser, Furkan

doi:10.1016/j.asoc.2020.106535

Cited by 28 publications

(15 citation statements)

References 41 publications

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“…This approach is often used to eliminate data points that are inconsistent with other members in the same data set. The existence of outliers in the data degrades machine learning predictions, potentially leading to incorrect conclusions [62]. For real-world data, extreme weather conditions were identified as outliers [63] For this reason, significant outliers in data features were removed using the Z score method [64].…”

Section: Data Cleaningmentioning

confidence: 99%

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

Khan

Kamaruddin

Sheikh

et al. 2022

Plants

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Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data.

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Section: Data Cleaningmentioning

confidence: 99%

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

Khan

Kamaruddin

Sheikh

et al. 2022

Plants

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“…First, the model requires sufficient and diverse data records to learn adequate input-output correlations (e.g., pH-dissolution rate). Second, outliers should be included in the database to ensure that the DF model comprehensively learns input-output correlations [63,64]. Herein, the outliers indicated that one or more data-recordsalthough measured and reported properly-did not fit into the trends exhibited by the majority of the data records in the neighborhood because of some underlying (chemical, or kinetic, or thermodynamic) mechanism.…”

Section: Predictions From Deep Forest Modelmentioning

confidence: 99%

Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models

et al. 2022

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The dissolution kinetics of Portland cement is a critical factor in controlling the hydration reaction and improving the performance of concrete. Tricalcium silicate (C3S), the primary phase in Portland cement, is known to have complex dissolution mechanisms that involve multiple reactions and changes to particle surfaces. As a result, current analytical models are unable to accurately predict the dissolution kinetics of C3S in various solvents when it is undersaturated with respect to the solvent. This paper employs the deep forest (DF) model to predict the dissolution rate of C3S in the undersaturated solvent. The DF model takes into account several variables, including the measurement method (i.e., reactor connected to inductive coupled plasma spectrometer and flow chamber with vertical scanning interferometry), temperature, and physicochemical properties of solvents. Next, the DF model evaluates the influence of each variable on the dissolution rate of C3S, and this information is used to develop a closed-form analytical model that can predict the dissolution rate of C3S. The coefficients and constant of the analytical model are optimized in two scenarios: generic and alkaline solvents. The results show that both the DF and analytical models are able to produce reliable predictions of the dissolution rate of C3S when it is undersaturated and far from equilibrium.

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“…the Euclidean distance between them is given by: [4,10], then D = 5. Furthermore, definition ( 12) is inspired by the representation of complex numbers and assumes that the CI endpoints can be seen as a point in ℜ 2 according to the EP diagram [59] (see Fig.…”

Section: Least Squares Cirmentioning

confidence: 99%

Interval-valued fuzzy regression: Philosophical and methodological issues

Boukezzoula

Coquin

2021

Applied Soft Computing

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Fuzzy regression functions with a noise cluster and the impact of outliers on mainstream machine learning methods in the regression setting

Cited by 28 publications

References 41 publications

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

Predicting Dissolution Kinetics of Tricalcium Silicate Using Deep Learning and Analytical Models

Interval-valued fuzzy regression: Philosophical and methodological issues

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