Online Consumer Alignment using Supervised Machine Learning Technique

Sorker, Md. Abdul Wadud; Siddika, Aysha; Titly, Tanjia Abedin; Mia, Md. Jueal; Bijoy, Md. Hasan Imam

doi:10.1109/icccnt54827.2022.9984234

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Efficiency: The PART algorithm is computationally efficient since it does not try to locate the globally optimal attribute at each node; instead, it selects the attribute that gives the best accuracy on the current subset of data [28,29].…”

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confidence: 99%

Partial Decision Tree Forest: A Machine Learning Model for the Geosciences

2023

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As a result of the continuous growth in the amount of geological data, machine learning (ML) offers an opportunity to contribute to solving problems in geosciences. However, digital geology applications introduce new challenges for machine learning due to the unique geoscience properties encountered in each problem, requiring novel research in ML. This paper proposes a novel machine learning method, entitled “Partial Decision Tree Forest (PART Forest)”, to overcome these challenges introduced by geoscience problems and offers potential advancements in both machine learning and geoscience disciplines. The effectiveness of the proposed PART Forest method was illustrated in mineral classification. This study aims to build an intelligent ML model that automatically classifies the minerals in terms of their crystal structures (triclinic, monoclinic, orthorhombic, tetragonal, hexagonal, and trigonal) by taking into account their chemical compositions and their physical and optical properties. In the experiments, the proposed PART Forest method demonstrated its superiority over one of the well-known ensemble learning methods, random forest, in terms of accuracy, precision, recall, f-score, and AUC (area under the curve) metrics.

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confidence: 99%