Explaining anomalies in coal proximity and coal processing data with Shapley and tree-based models

Explaining anomalies in coal proximity and coal processing data with Shapley and tree-based models

Modelling the characteristics and composition of coal is important, as proximity data and other measurements to do so are typically expensive or hard to acquire in real-time. Understanding anomalies in these relatively small data sets are important, as removal may result in an unnecessary loss of da...

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Bibliographic Details
Main Authors: Liu, Xiu, Aldrich, Chris
Format: Journal Article
Language:English
Published: Elsevier 2022
Subjects:
Science & Technology
Technology
Energy & Fuels
Engineering, Chemical
Engineering
Anomaly detection
Isolation forest
Shapley value regression
Coal
Variable importance measures
Random forests
PRINCIPAL COMPONENT ANALYSIS
BITUMINOUS COAL
COMBUSTION
SYSTEM
PREDICTION
REGRESSION
BOILER
FOREST
CARBON
ASH
Online Access:http://hdl.handle.net/20.500.11937/97646

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