Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models

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Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) techniques have been employed, no research compares...

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Bibliographic Details
Main Authors:	Pham, Hoa Thi, Awange, Joseph, Kuhn, Michael
Format:	Journal Article
Language:	English
Published:	MDPI 2022
Subjects:	Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering feature selection feature extraction machine learning crop yield VCI TCI VEGETATION HEALTH INDEXES FEATURE-SELECTION NEURAL-NETWORKS DROUGHT Algorithms Forecasting Machine Learning Neural Networks, Computer Support Vector Machine
Online Access:	http://hdl.handle.net/20.500.11937/91904

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