Relevant bibliographies by topics / Gaussian process classification model with multiclass

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Gaussian process classification model with multiclass'

Author: Grafiati
Published: 3 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Gaussian process classification model with multiclass"

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Girolami, Mark, and Simon Rogers. "Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors." Neural Computation 18, no. 8 (2006): 1790–817. http://dx.doi.org/10.1162/neco.2006.18.8.1790.

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It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over regression coefficients in favor of gaussian process (GP) priors over functions, and employing variational approximations to the full posterior, we obtain efficient computational methods for GP classification in the multiclass setting.1 The model augmentation with additional latent variables ensures full a posteriori class coupling while retaining the simple a priori independent GP covariance structure from which sparse approximations, such as multiclass informative vector machines (IVM), emerge in a natural and straightforward manner. This is the first time that a fully variational Bayesian treatment for multiclass GP classification has been developed without having to resort to additional explicit approximations to the nongaussian likelihood term. Empirical comparisons with exact analysis use Markov Chain Monte Carlo (MCMC) and Laplace approximations illustrate the utility of the variational approximation as a computationally economic alternative to full MCMC and it is shown to be more accurate than the Laplace approximation.
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Chatzis, Sotirios P. "A latent variable Gaussian process model with Pitman–Yor process priors for multiclass classification." Neurocomputing 120 (November 2013): 482–89. http://dx.doi.org/10.1016/j.neucom.2013.04.029.

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Zhao, Qibin, Liqing Zhang, and Andrzej Cichocki. "A Tensor-Variate Gaussian Process for Classification of Multidimensional Structured Data." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1041–47. http://dx.doi.org/10.1609/aaai.v27i1.8568.

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As tensors provide a natural and efficient representation of multidimensional structured data, in this paper, we consider probabilistic multinomial probit classification for tensor-variate inputs with Gaussian processes (GP) priors placed over the latent function. In order to take into account the underlying multimodes structure information within the model, we propose a framework of probabilistic product kernels for tensorial data based on a generative model assumption. More specifically, it can be interpreted as mapping tensors to probability density function space and measuring similarity by an information divergence. Since tensor kernels enable us to model input tensor observations, the proposed tensor-variate GP is considered as both a generative and discriminative model. Furthermore, a fully variational Bayesian treatment for multiclass GP classification with multinomial probit likelihood is employed to estimate the hyperparameters and infer the predictive distributions. Simulation results on both synthetic data and a real world application of human action recognition in videos demonstrate the effectiveness and advantages of the proposed approach for classification of multiway tensor data, especially in the case that the underlying structure information among multimodes is discriminative for the classification task.
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Cho, Wanhyun, Sangkyoon Kim, and Soonyoung Park. "New Inference for a Multiclass Gaussian Process Classification Model using a Variational Bayesian EM Algorithm and Laplace Approximation." IEIE Transactions on Smart Processing and Computing 4, no. 4 (2015): 202–8. http://dx.doi.org/10.5573/ieiespc.2015.4.4.202.

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Kamau, J. N., P. K. Hinga, and S. I. Kamau. "Support Vector Machine Kernel Model Calibration for Optimal Accuracy in Automatic Pineapple Slices Classification." International Research Journal of Innovations in Engineering and Technology 06, no. 09 (2022): 01–8. http://dx.doi.org/10.47001/irjiet/2022.609001.

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Sorting pineapple can be automated with use of computer vision. The unique challenge with the pineapple slices is variability of the fruit slices color, ripeness and texture due to varying environmental parameters and fruit types. The most common types of pineapple fruit are smooth Caen and MD2. Currently the pineapple industries sort the slices manually using casual workers. Before commencement of a typical production shift, there is startup shift where machine are cleaned, prepared and calibrated for the production. Fruit slices are also sampled and processed to simulated actual production. A mock sorting is done to help guide the worker for the expected sorting for the five categories i.e: fancy ¾, fancy ½, choice, broken and reject. To achieve a fully automated sorting process there is a need to calibrate machine model and capture the day to day variability of fruit color, ripeness and texture. In this paper we propose to use an analytical method to calibrate the Support Vector Machine (SVM) with Gaussian radial basis function (RBF) for optimal sigma and box constraint (C). A compelling feature of the proposed algorithm is that it does not require an optimization search, making the selection process simpler and more computationally efficient. The proposed algorithm achieves the highest accuracy when used with the Gaussian multiclass SVM, as demonstrated by experimental results on three real-world datasets.
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Rekha, S. N., Aruna Jeyanthy P., and Devaraj D. "Relevance vector machine based fault classification in wind energy conversion system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 1506–13. https://doi.org/10.11591/ijece.v9i3.pp1506-1513.

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This Paper is an attempt to develop the multiclass classification in the Benchmark fault model applied on wind energy conversion system using the relevance vector machine (RVM). The RVM could apply a structural risk minimization by introducing a proper kernel for training and testing. The Gaussian Kernel is used for this purpose. The classification of sensor, process and actuators faults are observed and classified in the implementation. Training different fault condition and testing is carried out using the RVM implementation carried out using Matlab on the Wind Energy Conversion System (WECS). The training time becomes important while the training is carried out in a bigger WECS, and the hardware feasibility is prime while the testing is carried out on an online fault detection scenario. Matlab based implementation is carried out on the benchmark model for the fault detection in the WECS. The results are compared with the previously implemented fault detection technique and found to be performing better in terms of training time and hardware feasibility.
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Adi Pratama, I. Putu, Ery Setiyawan Jullev Atmadji, Dwi Amalia Purnamasar, and Edi Faizal. "Evaluating the Performance of Voting Classifier in Multiclass Classification of Dry Bean Varieties." Indonesian Journal of Data and Science 5, no. 1 (2024): 23–29. http://dx.doi.org/10.56705/ijodas.v5i1.124.

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This study explores the application of a voting classifier, integrating Decision Tree, Logistic Regression, and Gaussian Naive Bayes models, for the multiclass classification of dry bean varieties. Utilizing a dataset comprising 13,611 images of dry bean grains, captured through a high-resolution computer vision system, we extracted 16 features to train and test the classifier. Through a rigorous 5-fold cross-validation process, we assessed the model's performance, focusing on accuracy, precision, recall, and F1-score metrics. The results demonstrated significant variability in the classifier's performance across different data subsets, with accuracy rates fluctuating between 31.23% and 96.73%. This variability highlights the classifier's potential under specific conditions while also indicating areas for improvement. The research contributes to the agricultural informatics field by showcasing the effectiveness and challenges of using ensemble learning methods for crop variety classification, a crucial task for enhancing agricultural productivity and food security. Recommendations for future research include exploring additional features to improve model generalization, extending the dataset for broader applicability, and comparing the voting classifier's performance with other ensemble methods or advanced machine learning models. This study underscores the importance of machine learning in advancing agricultural classification tasks, paving the way for more efficient and accurate crop sorting and grading processes.
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N., Rekha S., P. Aruna Jeyanthy, and D. Devaraj. "Relevance vector machine based fault classification in wind energy conversion system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 1506. http://dx.doi.org/10.11591/ijece.v9i3.pp1506-1513.

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<p>This Paper is an attempt to develop the multiclass classification in the Benchmark fault model applied on wind energy conversion system using the relevance vector machine (RVM). The RVM could apply a structural risk minimization by introducing a proper kernel for training and testing. The Gaussian Kernel is used for this purpose. The classification of sensor, process and actuators faults are observed and classified in the implementation. Training different fault condition and testing is carried out using the RVM implementation carried out using Matlab on the Wind Energy Conversion System (WECS). The training time becomes important while the training is carried out in a bigger WECS, and the hardware feasibility is prime while the testing is carried out on an online fault detection scenario. Matlab based implementation is carried out on the benchmark model for the fault detection in the WECS. The results are compared with the previously implemented fault detection technique and found to be performing better in terms of training time and hardware feasibility.</p>
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Wu, Zhiyong, Xiangqian Ding, and Guangrui Zhang. "A Novel Method for Classification of ECG Arrhythmias Using Deep Belief Networks." International Journal of Computational Intelligence and Applications 15, no. 04 (2016): 1650021. http://dx.doi.org/10.1142/s1469026816500218.

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In this paper, a novel approach based on deep belief networks (DBN) for electrocardiograph (ECG) arrhythmias classification is proposed. The construction process of ECG classification model consists of two steps: features learning for ECG signals and supervised fine-tuning. In order to deeply extract features from continuous ECG signals, two types of restricted Boltzmann machine (RBM) including Gaussian–Bernoulli and Bernoulli–Bernoulli are stacked to form DBN. The parameters of RBM can be learned by two training algorithms such as contrastive divergence and persistent contrastive divergence. A suitable feature representation from the raw ECG data can therefore be extracted in an unsupervised way. In order to enhance the performance of DBN, a fine-tuning process is carried out, which uses backpropagation by adding a softmax regression layer on the top of the resulting hidden representation layer to perform multiclass classification. The method is then validated by experiments on the well-known MIT-BIH arrhythmia database. Considering the real clinical application, the inter-patient heartbeat dataset is divided into two sets and grouped into four classes (N, S, V, F) following the recommendations of AAMI. The experiment results show our approach achieves better performance with less feature learning time than traditional hand-designed methods on the classification of ECG arrhythmias.
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Hosenie, Zafiirah, Robert Lyon, Benjamin Stappers, Arrykrishna Mootoovaloo, and Vanessa McBride. "Imbalance learning for variable star classification." Monthly Notices of the Royal Astronomical Society 493, no. 4 (2020): 6050–59. http://dx.doi.org/10.1093/mnras/staa642.

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ABSTRACT The accurate automated classification of variable stars into their respective subtypes is difficult. Machine learning–based solutions often fall foul of the imbalanced learning problem, which causes poor generalization performance in practice, especially on rare variable star subtypes. In previous work, we attempted to overcome such deficiencies via the development of a hierarchical machine learning classifier. This ‘algorithm-level’ approach to tackling imbalance yielded promising results on Catalina Real-Time Survey (CRTS) data, outperforming the binary and multiclass classification schemes previously applied in this area. In this work, we attempt to further improve hierarchical classification performance by applying ‘data-level’ approaches to directly augment the training data so that they better describe underrepresented classes. We apply and report results for three data augmentation methods in particular: Randomly Augmented Sampled Light curves from magnitude Error (RASLE), augmenting light curves with Gaussian Process modelling (GpFit) and the Synthetic Minority Oversampling Technique (SMOTE). When combining the ‘algorithm-level’ (i.e. the hierarchical scheme) together with the ‘data-level’ approach, we further improve variable star classification accuracy by 1–4 per cent. We found that a higher classification rate is obtained when using GpFit in the hierarchical model. Further improvement of the metric scores requires a better standard set of correctly identified variable stars, and perhaps enhanced features are needed.
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Dissertations / Theses on the topic "Gaussian process classification model with multiclass"

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Ringdahl, Benjamin. "Gaussian Process Multiclass Classification : Evaluation of Binarization Techniques and Likelihood Functions." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87952.

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In binary Gaussian process classification the prior class membership probabilities are obtained by transforming a Gaussian process to the unit interval, typically either with the logistic likelihood function or the cumulative Gaussian likelihood function. Multiclass classification problems can be handled by any binary classifier by means of so-called binarization techniques, which reduces the multiclass problem into a number of binary problems. Other than introducing the mathematics behind the theory and methods behind Gaussian process classification, we compare the binarization techniques one-against-all and one-against-one in the context of Gaussian process classification, and we also compare the performance of the logistic likelihood and the cumulative Gaussian likelihood. This is done by means of two experiments: one general experiment where the methods are tested on several publicly available datasets, and one more specific experiment where the methods are compared with respect to class imbalance and class overlap on several artificially generated datasets. The results indicate that there is no significant difference in the choices of binarization technique and likelihood function for typical datasets, although the one-against-one technique showed slightly more consistent performance. However the second experiment revealed some differences in how the methods react to varying degrees of class imbalance and class overlap. Most notably the logistic likelihood was a dominant factor and the one-against-one technique performed better than one-against-all.
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Book chapters on the topic "Gaussian process classification model with multiclass"

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Cho, Wanhyun, Soonyoung Park, and Sangkyoon Kim. "Multiclass Data Classification Using Multinomial Logistic Gaussian Process Model." In Advances in Computer Science and Ubiquitous Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-7605-3_21.

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Ahmed, Eman, Neamat El Gayar, Amir F. Atiya, and Iman A. El Azab. "Fuzzy Gaussian Process Classification Model." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02611-9_37.

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Cho, Wanhyun, Soonja Kang, Sangkyoon Kim, and Soonyoung Park. "Variational Bayesian Inference for Multinomial Dirichlet Gaussian Process Classification Model." In Advances in Computer Science and Ubiquitous Computing. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0281-6_117.

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Meng, Rui, Herbert K. H. Lee, and Kristofer Bouchard. "Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_28.

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AbstractThis paper presents an efficient variational inference framework for a family of structured Gaussian process regression network (SGPRN) models. We incorporate auxiliary inducing variables in latent functions and jointly treat both the distributions of the inducing variables and hyper-parameters as variational parameters. Then we take advantage of the collapsed representation of the model and propose structured variational distributions, which enables the decomposability of a tractable variational lower bound and leads to stochastic optimization. Our inference approach is able to model data in which outputs do not share a common input set, and with a computational complexity independent of the size of the inputs and outputs to easily handle datasets with missing values. Finally, we illustrate our approach on both synthetic and real data.
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Klein, Julia, and Tatjana Petrov. "Understanding Social Feedback in Biological Collectives with Smoothed Model Checking." In Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19759-8_12.

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AbstractBiological groups exhibit fascinating collective dynamics without centralised control, through only local interactions between individuals. Desirable group behaviours are typically linked to a certain fitness function, which the group robustly performs under different perturbations in, for instance, group structure, group size, noise, or environmental factors. Deriving this fitness function is an important step towards understanding the collective response, yet it easily becomes non-trivial in the context of complex collective dynamics. In particular, understanding the social feedback - how the collective behaviour adapts to changes in the group size - requires dealing with complex models and limited experimental data. In this work, we assume that the collective response is experimentally observed for a chosen, finite set of group sizes. Based on such data, we propose a framework which allows to: (i) predict the collective response for any given group size, and (ii) automatically propose a fitness function. We use Smoothed Model Checking, an approach based on Gaussian Process Classification, to develop a methodology that is scalable, flexible, and data-efficient; We specify the fitness function as a template temporal logic formula with unknown parameters, and we automatically infer the missing quantities from data. We evaluate the framework over a case study of a collective stinging defence mechanism in honeybee colonies.
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Neal, Radford M. "Regression and Classification Using Gaussian Process Priors." In Bayesian Statistics 6. Oxford University PressOxford, 1999. http://dx.doi.org/10.1093/oso/9780198504856.003.0021.

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Abstract Gaussian processes are a natural way of specifying prior distributions over functions of one or more input variables. When such a function defines the mean response in a regression model with Gaussian errors, inference can be done using matrix computations, which are feasible for datasets of up to about a thousand cases. The covariance function of the Gaussian process can be given a hierarchical prior, which allows the model to discover high-level properties of the data, such as which inputs are relevant to predicting the response. Inference for these covariance hyperparameters can be done using Markov chain sampling. Classification models can be defined using Gaussian processes for underlying latent values, which can also be sampled within the Markov chain. Gaussian processes are in my view the simplest and most obvious way of defining flexible Bayesian regression and classification models, but despite some past usage, they appear to have been rather neglected as a general-purpose technique. This may be partly due to a confusion between the properties of the function being modeled and the properties of the best predictor for this unknown function.
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Cherukuri, Aswani Kumar, Karan Bhowmick, Firuz Kamalov, and Chee Ling Thong. "A Comparative Analysis of Urban Transport Using K-Means Clustering and Multi-Class Classification." In Advances in Information Security, Privacy, and Ethics. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5250-9.ch013.

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The transportation planning process requires a comprehensive study of the regions that need development. This study is an extension of the methodology of transportation planning. The authors use real-time data from Foursquare API to map out the number of transportation facilities and infrastructure available for each city. This study will shed light on areas that need the most development in terms of intra-neighbourhood and inter-neighbourhood transportation. We use k-means clustering to organize and visualize clusters based on a calculated metric called “Availability Factor” that they have defined, and the number of transportation facilities available in each neighbourhood. Finally, they use the data at hand to create a model for multiclass classification to segregate new data into the predefined classes produced by the unsupervised learning model. The information procured in this work can be used to assess the quality of transportation available in the neighbourhoods of a location and help identify key areas for development.
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Tiwari, Shamik. "An Analysis in Tissue Classification for Colorectal Cancer Histology Using Convolution Neural Network and Colour Models." In Deep Learning and Neural Networks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch038.

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Computer vision-based identification of different tissue categories in histological images is a critical application of the computer-assisted diagnosis (CAD). Computer-assisted diagnosis systems support to reduce the cost and increase the efficiency of this process. Traditional image classification approaches depend on feature extraction methods designed for a specific problem based on domain information. Deep learning approaches are becoming important alternatives with advance of machine learning technologies to overcome the numerous difficulties of the feature-based approaches. A method for the classification of histological images of human colorectal cancer containing seven different types of tissue using convolutional neural network (CNN) is proposed in this article. The method is evaluated using four different colour models in absence and presence of Gaussian noise. The highest classification accuracies are achieved with HVI colour model, which is 95.8% in nonexistence and 78.5% in existence of noise respectively.
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Roy, Sangeeta, J. Jagan, and Pijush Samui. "Determination of Work Zone Capacity Using ELM, MPMR and GPR." In Advances in Civil and Industrial Engineering. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8648-9.ch004.

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This article examines the capability of Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) and Gaussian Process Regression (GPR) for determination of Work Zone Capacity. Number of lanes, number of open lanes, work zone layout, length, lane width, percentage trucks, grade, speed, work intensity, darkness factor, and proximity of ramps have been adopted as inputs of ELM, MPMR and GPR. ELM has excellent generalization performance, rapid training speed and little human intervention. MPMR is developed based on the concept of minimax probability machine classification. It does not assume any data distribution. GPR is a probabilistic, and non-parametric model. In GPR, different kinds of prior knowledge can be applied. This article describes a comparative study between the ELM, MPMR and GPR models.
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Yang, Jian, Weilin Wang, and Yang Yu. "Research on the Classification Model of Gas Well Water Production Driven by Production Data of Shale Gas Cage Type Throttle Valve." In Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde230403.

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Water production from gas wells is an important factor affecting gas well productivity. The excessive fluctuation of pressure after the wellhead water flows interfere with the pressure control gas production process, making it difficult to accurately control the wellhead pressure as a control target and thus affecting productivity, this paper carried out research on shale gas cage type throttle valve production data-driven gas well water production classification model. Firstly, the pre valve temperature, pre valve pressure, post valve temperature, and post valve pressure data from 6 wells on site were collected and preprocessed. Secondly, 39 kinds of data classification algorithms such as precise tree, linear discrimination, logistic regression, and Gaussian naive Bayes were used to predict the water production of the cage type throttle valve. Finally, the judgment results are directly used in the cage type throttle valve control system to solve the problem of real-time synchronization of data caused by the wellhead metering mechanism. The research results show that due to the complex characteristics of gas-liquid two-phase throttling at the wellhead of shale gas wells, the adaptability of traditional statistical classification models to the judgment of shale gas well water production is different. Through experimental verification, the optimized KNN classification model is more suitable for shale gas cage throttle valve production data processing. Based on data-driven methods, this paper can directly determine the water production situation of the throttle valve, and thus achieve real-time adjustment, laying the foundation for remote control of the throttle valve. The research results have important engineering value for improving the production efficiency and automation level of shale gas wells in Sichuan.
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Conference papers on the topic "Gaussian process classification model with multiclass"

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Almusawi, Muntadher Muhssan, Gotlur Karuna, E. Suganya, Bhanu Chandar Yenugu, and Thamizhkani B. "Detection and Classification of Faults in Dynamically Controlled Systems using Gaussian Process Regression with Hidden Markov Model." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872122.

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Adeeyo, Y. A., A. A. Osinaike, G. O. Adun, and H. Menkiti. "Identifying Litho-Fluid Facies Using Machine Learning: A Case Study of Niger Delta Sandstone Reservoirs." In Offshore Technology Conference Brasil. OTC, 2023. http://dx.doi.org/10.4043/32848-ms.

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Abstract To minimize uncertainties in reservoir characterization and modeling workflows, an accurate definition of reservoir heterogeneity, including lithologic and pore fluid variations, is required. Particularly because reservoir heterogeneity limits the spatial distribution of petrophysical and elastic properties used to understand the reservoir system or quantify its resource or storage potential. Traditionally, variations in reservoir rock and fluid properties are identified by interpreting depositional patterns and fluid content from core or cutting samples, or by applying statistical rock physics techniques to elastic well log data. However, collecting core data is costly, and both methods can be subjective, necessitating expert knowledge of sedimentological and rock physical principles to produce meaningful classification results. To overcome these limitations, we present a cost-effective and comparatively objective framework for identifying litho-fluid facies (LFF) using machine learning (ML) algorithms. Various statistical ML techniques were used for data-driven delineation of the LFF in the Niger Delta siliciclastic formation from a suite of commonly available geophysical well logs. The study followed a two-part process to arrive at the desired outcome. First, the target classes—clean hydrocarbon sand, shaly hydrocarbon sand, brine sand, and shale—were generated using a Dirichlet Process Gaussian Mixture Model (DPGMM) with Variational Inference (VI), an unsupervised clustering technique. These classes were subjected to probabilistic thresholding based on their log-likelihood and silhouette coefficient scores to obtain high-quality training samples. Following that, the training samples were used to construct supervised multiclass predictive models capable of generalizing the target LFF. Several classification metrics and charts were used to assess the accuracy and speed of the models to determine the model with the best predictive and computational performance. This ultimately revealed that the best-performing model was a single decision tree classifier with perfect metric scores, significantly high prediction probabilities, and minimal computational time. The random forest and gradient boosting classifiers performed similarly well on the task. Moreover, the use of analytical and statistical techniques throughout the process facilitated an objective and accurate differentiation of the rock and fluid types. The ability of the models to generalize to unseen data in a new well location with high predictive confidence makes it possible to characterize the spatially distributed facies in the study area with minimized uncertainty. Hence, we recommend the adoption of this framework for rapid and accurate LFF identification.
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Adeeyo, Y. A., and A. A. Osinaike. "Identifying Litho-Fluid Facies Using Machine Learning: A Case Study of Niger Delta Sandstone Reservoirs." In SPE Advances in Integrated Reservoir Modelling and Field Development Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/225301-ms.

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Abstract To minimize uncertainties in reservoir characterization and modeling workflows, an accurate definition of reservoir heterogeneity, including lithologic and pore fluid variations, is required. Particularly because reservoir heterogeneity limits the spatial distribution of petrophysical and elastic properties used to understand the reservoir system or quantify its resource or storage potential. Traditionally, variations in reservoir rock and fluid properties are identified by interpreting depositional patterns and fluid content from core or cutting samples, or by applying statistical rock physics techniques to elastic well log data. However, collecting core data is costly, and both methods can be subjective, necessitating expert knowledge of sedimentological and rock physical principles to produce meaningful classification results. To overcome these limitations, we present a cost-effective and comparatively objective framework for identifying litho-fluid facies (LFF) using machine learning (ML) algorithms. Various statistical ML techniques were used for data-driven delineation of the LFF in the Niger Delta siliciclastic formation from a suite of commonly available geophysical well logs. The study followed a two-part process to arrive at the desired outcome. First, the target classes—clean hydrocarbon sand, shaly hydrocarbon sand, brine sand, and shale—were generated using a Dirichlet Process Gaussian Mixture Model (DPGMM) with Variational Inference (VI), an unsupervised clustering technique. These classes were subjected to probabilistic thresholding based on their log-likelihood and silhouette coefficient scores to obtain high-quality training samples. Following that, the training samples were used to construct supervised multiclass predictive models capable of generalizing the target LFF. Several classification metrics and charts were used to assess the accuracy and speed of the models to determine the model with the best predictive and computational performance. This ultimately revealed that the best-performing model was a single decision tree classifier with perfect metric scores, significantly high prediction probabilities, and minimal computational time. The random forest and gradient boosting classifiers performed similarly well on the task. Moreover, the use of analytical and statistical techniques throughout the process facilitated an objective and accurate differentiation of the rock and fluid types. The ability of the models to generalize to unseen data in a new well location with high predictive confidence makes it possible to characterize the spatially distributed facies in the study area with minimized uncertainty. Hence, we recommend the adoption of this framework for rapid and accurate LFF identification.
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Urtasun, Raquel, and Trevor Darrell. "Discriminative Gaussian process latent variable model for classification." In the 24th international conference. ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273613.

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Berns, Fabian, Joschka Hannes Strueber, and Christian Beecks. "Local Gaussian Process Model Inference Classification for Time Series Data." In SSDBM 2021: 33rd International Conference on Scientific and Statistical Database Management. ACM, 2021. http://dx.doi.org/10.1145/3468791.3468839.

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Varadarajan, Jagannadan, Ramanathan Subramanian, Narendra Ahuja, Pierre Moulin, and Jean-Marc Odobez. "Active Online Anomaly Detection Using Dirichlet Process Mixture Model and Gaussian Process Classification." In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017. http://dx.doi.org/10.1109/wacv.2017.74.

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Li, Jinxing, Bob Zhang, Guangming Lu, and David Zhang. "Shared Linear Encoder-based Gaussian Process Latent Variable Model for Visual Classification." In MM '18: ACM Multimedia Conference. ACM, 2018. http://dx.doi.org/10.1145/3240508.3240520.

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Bertrand, Hadrien, Matthieu Perrot, Roberto Ardon, and Isabelle Bloch. "Classification of MRI data using deep learning and Gaussian process-based model selection." In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE, 2017. http://dx.doi.org/10.1109/isbi.2017.7950626.

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He, Peng, Li-Ping Li, Qian-Qing Zhang, Fei Xu, Jie Hu, and Jian Zhang. "Gaussian Process Model of an Advanced Surrounding Rock Classification Based on Tunnel Seismic Predictions." In Fourth Geo-China International Conference. American Society of Civil Engineers, 2016. http://dx.doi.org/10.1061/9780784480038.026.

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10

Sun, Haotian. "Application of Gaussian Process Classification to the Integrity Management of Energy Pipelines." In 2024 15th International Pipeline Conference. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/ipc2024-134103.

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Abstract:
Abstract Leveraging algorithms that autonomously evolve with data, machine learning has become integral and been extensively applied to the pipeline integrity management practice to create accurate predictive models, whose outputs can either be numerical or categorical. The latter corresponds to the classification analysis, whose models typically yield deterministic outputs, categorizing data points as of either the positive or negative class. However, practical engineering scenarios may demand a more nuanced approach. Additionally, the conventional “black-box” nature of many machine learning models often hinders their interpretability. This study introduces Gaussian Process Classification (GPC) — a probabilistic classification algorithm that has been found to be underutilized — to the realm of pipeline integrity management, where challenges in the classification accuracy and efficiency of specific problems have become noteworthy. Unlike some classifiers, GPC provides the probability that a data point belongs to the positive class, allowing for a more flexible decision-making process. Notably, users can self-determine the threshold probability. Moreover, the predictive process of GPC is analytically transparent, enhancing the overall interpretability of the machine learning model. The algorithm is applied to three distinct binary classification problems within pipeline integrity management: separating failure modes for cracked pipelines, differentiating failure modes for corroded pipelines, and predicting ignition upon rupture of onshore natural gas transmission pipelines, while each problem involves unique input features. The machine learning models for these problems undergo thorough training via hyperparameter tuning using the maximum likelihood method. This approach improves model robustness, mitigates overfitting, and optimizes performance. The developed GPC models are benchmarked against existing mechanics-based and log-logistic models for comparative evaluation, whose results demonstrate the accuracy and superiority of GPC in these specific scenarios. The present study demonstrates the value of probabilistic machine learning for improving the pipeline integrity management practice.
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