.png){kind=icon}
Return
A systematic evaluation of data preprocessing and model optimization for machine learning algorithms: Using sphalerite trace element data as an example
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jseaes.2025.106728.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• The KNN imputation has a relatively small impact on the data structure. • Centered log-ratio or Log-transformation is effective for PCA, t-SNE, LDA, PLS-DA, and SVM. • Feature selection can optimize algorithm and enhance the interpretability of results. • Grid search and cross-validation can effectively enhance the accuracy of the algorithm. • XGBoost demonstrates superior classification performance on unfilled data.
Journal
IF:
2.4
Papers:
654
Citations:
1.9W
Organization
No organization information available
Cited Papers
Centered Log-Ratio (clr) Transformation and Robust Principal Component Analysis of Long-Term NDVI Data Reveal Vegetation Activity Linked to Climate Processes.png)
Climate
OAAI
ClimateIF0
OAAIShareSave
ShareSaveDistinguishing Ore Deposit Type and Barren Sedimentary Pyrite Using Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Trace Element Data and Statistical Analysis of Large Data SetsECONOMIC GEOLOGYOAAI
ECONOMIC GEOLOGYIF4.9
OAAIShareSaveTaking the Human Out of the Loop: A Review of Bayesian OptimizationPROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
ShareSaveTrace Element Composition of Igneous and Hydrothermal Magnetite from Porphyry Deposits: Relationship to Deposit Subtypes and Magmatic AffinityECONOMIC GEOLOGY
ECONOMIC GEOLOGYIF4.9
PREAI
ShareSaveShareSaveShareSaveShareSave
View more