Selecting key predictor parameters for regression modelling using modified Neighbourhood Component Analysis (NCA) Algorithm
Files
(Published version)
Date
2020
Authors
Amankwaa Kyeremeh, B.
Greet, C.
Zanin, M.
Skinner, W.
Asamoah, R.K.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of 6th UMaT Biennial International Mining and Mineral Conference, 2020, pp.320-325
Statement of Responsibility
Conference Name
6th UMaT Biennial International Mining and Mineral Conference (5 Aug 2020 - 6 Aug 2020 : Tarkwa, Ghana)
Abstract
Selecting the most useful features for the purpose of regression analysis is very critical in ensuring good prediction. In this research, modified Neighbourhood Component Analysis (NCA) algorithm has been used as a feature selection criterion for selecting the most relevant parameters from 25 rougher flotation parameters. Predictor parameters selected to be relevant for regression analysis included throughput, feed particle size, frother dosage, xanthate dosage and froth depth as confirmed in literature. This result is a clear indication that modified NCA Algorithm can select relevant features for the purpose of regression analysis.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2020 The author(s).