Geochemical Data Exploration using Machine Learning Methods

Title

Geochemical Data Exploration using Machine Learning Methods

Creator

Shrivastava, Abhay; George, Jossy P; Paul Alapatt, Bosco

Description

This study introduces a novel ensemble model combining Support Vector Machine (SVM) and Gradient Boosting algorithm (GBC). The model's performance is compared with the two single layered model namely K-Nearest Neighbors (KNN) and Gaussian Naive Bayes (GNB) on a publicly available dataset. Further, Performance is measured using standard metrics such as accuracy, precision, and recall. To have the excellence in detection of types of rocks based on its properties this research explores the stacking approach, contributing in the field of geological studies and also for future exploration making it effective and efficient in identification of mineral deposits. 2025 IEEE.

Source

Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.484-489

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Classification; Gaussian Nae Bayes; Geochemical exploration; Gradient Boosting algorithm; igneous rocks; KNN; Machine learning; Stacking and ensemble; SVM

Coverage

Shrivastava A., Christ University, India; George J.P., Christ University, India; Paul Alapatt B., Christ University, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833151175-3;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICICV64824.2025.11085828

https://www.scopus.com/pages/publications/105013073769?origin=resultslist