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Machine Learning for Mining & Metals

version (17.3 MB) by David Willingham
2 examples for Machine Learning for Mining, Regression & Classification


Updated 01 Sep 2016

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As presented in the Webinar "Machine Learning for the Mining Industry"
Example 1:
Goal is to build a model that can detect defects in steel plate
This demo shows:
a) Machine Learning techniques (Neural Networks, Naive Bayesian, Tree Bagger), Feature Selection,
b)Parallel Computing
c) MATLAB Compiler & Builder Ex
d) Spreadsheet Link
Example 2:
This demo shows how machine learning can be used to improve the accuracy of modelling and predicting the impurities output of an iron ore processing plant. A number of variables in the plant were measured over time including the silica (SiO2) and magnesia (MgO) concentration at the output of the plant. The goal of the modelling is to determine what parameters in the plant need to be adjusted to keep the silica and magnesia concentration at the desired level. The demo uses 1 year of real data captured in an iron-ore processing plant.

This demo shows:
a) Preparing time series data for analysis
b) Interpolating missing data
c) Time aligning and joining multiple data sets using tables
d) Using decision trees and neural networks to improve the model calculated using multiple linear regression
e) Using sequential feature selection to identify the important parameters in the plant

Cite As

David Willingham (2022). Machine Learning for Mining & Metals (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2014a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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