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How to improve machine learning models for lithofacies identification by practical and novel ensemble strategy and principles

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Petroleum Science 2023年第2期20卷 733-752页

作者： Shao-Qun Dong Yan-Ming Sun Tao Xu Lian-Bo Zeng Xiang-Yi Du Xu Yang Yu Liang State Key Laboratory of Petroleum Resources and Prospecting China University of PetroleumBeijing102249China College of Science China University of PetroleumBeijing102249China College of Geoscience China University of PetroleumBeijing102249China

Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs labelled by rock cores. However, these methods have accuracy limits to some extent. To further improve their accuracies, practical and novel ensemble learning strategy and principles are proposed in this work, which allows geologists not familiar with ML to establish a good ML lithofacies identification model and help geologists familiar with ML further improve accuracy of lithofacies identification. The ensemble learning strategy combines ML methods as sub-classifiers to generate a comprehensive lithofacies identification model, which aims to reduce the variance errors in prediction. Each sub-classifier is trained by randomly sampled labelled data with random features. The novelty of this work lies in the ensemble principles making sub-classifiers just overfitting by algorithm parameter setting and sub-dataset sampling. The principles can help reduce the bias errors in the prediction. Two issues are discussed, videlicet (1) whether only a relatively simple single-classifier method can be as sub-classifiers and how to select proper ML methods as sub-classifiers;(2) whether different kinds of ML methods can be combined as sub-classifiers. If yes, how to determine a proper combination. In order to test the effectiveness of the ensemble strategy and principles for lithofacies identification, different kinds of machine learning algorithms are selected as sub-classifiers, including regular classifiers (LDA, NB, KNN, ID3 tree and CART), kernel method (SVM), and ensemble learning algorithms (RF, AdaBoost, XGBoost and LightGBM). In this work, the experiments used a published dataset of lithofacies from Daniudi gas field (DGF) in Ordes Basin, China. Based on a series of comparisons between ML algorithms and their corresponding ensemble models using the ensemble strategy and principles, conclusions are drawn: (1) n

关键词： Lithofacies identification Machine learning ensemble learning strategy ensemble principle homogeneous ensemble Heterogeneous ensemble

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ensemble Variable Selection for Naive Bayes to Improve Customer Behaviour Analysis

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Computer Systems Science & Engineering 2022年第4期41卷 339-355页

作者： R.Siva Subramanian D.Prabha Anna University Chennai600025India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efﬁcient and in-depth manner.Further investigation of customer patterns helps theﬁrm to develop efﬁcient decisions and in turn,helps to optimize the enter-prise’s business and maximizes consumer satisfaction correspondingly.To con-duct an effective assessment about the customers,Naive Bayes(also called Simple Bayes),a machine learning model is utilized.However,the efﬁcacious of the simple Bayes model is utterly relying on the consumer data used,and the existence of uncertain and redundant attributes in the consumer data enables the simple Bayes model to attain the worst prediction in consumer data because of its presumption regarding the attributes applied.However,in practice,the NB pre-mise is not true in consumer data,and the analysis of these redundant attributes enables simple Bayes model to get poor prediction results.In this work,an ensem-ble attribute selection methodology is performed to overcome the problem with consumer data and to pick a steady uncorrelated attribute set to model with the NB classiﬁer.In ensemble variable selection,two different strategies are applied:one is based upon data perturbation(or homogeneous ensemble,same feature selector is applied to a different subsamples derived from the same learning set)and the other one is based upon function perturbation(or heterogeneous ensemble different feature selector is utilized to the same learning set).Further-more,the feature set captured from both ensemble strategies is applied to NB indi-vidually and the outcome obtained is computed.Finally,the experimental outcomes show that the proposed ensemble strategies perform efﬁciently in choosing a steady attribute set and increasing NB classiﬁcation performance efﬁciently.

关键词： Naive bayes or simple bayes variable selection homogeneous ensemble heterogeneous ensemble customer prediction

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