Conduct Feature Selection Research Articles

MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model

A crucial mechanism for controlling the actions of proteins is allostery. Allosteric modulators have the potential to provide many benefits compared to orthosteric ligands, such as increased selectivity and saturability of their effect. The identification of new allosteric sites presents prospects for the creation of innovative medications and enhances our comprehension of fundamental biological mechanisms. Allosteric sites are increasingly found in different protein families through various techniques, such as machine learning applications, which opens up possibilities for creating completely novel medications with a diverse variety of chemical structures. Machine learning methods, such as PASSer, exhibit limited efficacy in accurately finding allosteric binding sites when relying solely on 3D structural information.Scientific ContributionPrior to conducting feature selection for allosteric binding site identification, integration of supporting amino-acid–based information to 3D structural knowledge is advantageous. This approach can enhance performance by ensuring accuracy and robustness. Therefore, we have developed an accurate and robust model called Multimodel Ensemble Feature Selection for Allosteric Site Identification (MEF-AlloSite) after collecting 9460 relevant and diverse features from the literature to characterise pockets. The model employs an accurate and robust multimodal feature selection technique for the small training set size of only 90 proteins to improve predictive performance. This state-of-the-art technique increased the performance in allosteric binding site identification by selecting promising features from 9460 features. Also, the relationship between selected features and allosteric binding sites enlightened the understanding of complex allostery for proteins by analysing selected features. MEF-AlloSite and state-of-the-art allosteric site identification methods such as PASSer2.0 and PASSerRank have been tested on three test cases 51 times with a different split of the training set. The Student’s t test and Cohen’s D value have been used to evaluate the average precision and ROC AUC score distribution. On three test cases, most of the p-values (<0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$< 0.05$$\\end{document}) and the majority of Cohen’s D values (>0.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$> 0.5$$\\end{document}) showed that MEF-AlloSite’s 1–6% higher mean of average precision and ROC AUC than state-of-the-art allosteric site identification methods are statistically significant.

Research on Hard Rock Pillar Stability Prediction Based on SABO-LSSVM Model

The increase in mining depth necessitates higher strength requirements for hard rock pillars, making mine pillar stability analysis crucial for pillar design and underground safety operations. To enhance the accuracy of predicting the stability state of mine pillars, a prediction model based on the subtraction-average-based optimizer (SABO) for hyperparameter optimization of the least-squares support vector machine (LSSVM) is proposed. First, by analyzing the redundancy of features in the mine pillar dataset and conducting feature selection, five parameter combinations were constructed to examine their effects on the performance of different models. Second, the SABO-LSSVM prediction model was compared vertically with classic models and horizontally with other optimized models to ensure comprehensive and objective evaluation. Finally, two data sampling methods and a combined sampling method were used to correct the bias of the optimized model for different categories of mine pillars. The results demonstrated that the SABO-LSSVM model exhibited good accuracy and comprehensive performance, thereby providing valuable insights for mine pillar stability prediction.

Development of an equation to predict delta bilirubin levels using machine learning

ObjectiveDelta bilirubin (albumin-covalently bound bilirubin) may provide important clinical utility in identifying impaired hepatic excretion of conjugated bilirubin, but it cannot be measured in real-time for diagnostic purposes in clinical laboratories. MethodsA total of 210 samples were collected, and their delta bilirubin levels were measured four times using high-performance liquid chromatography. Data collected included age, sex, diagnosis code, delta bilirubin, total bilirubin, direct bilirubin, total protein, albumin, globulin, aspartate aminotransferase, alanine transaminase, alkaline phosphatase, gamma-glutamyl transferase, lactate dehydrogenase, hemoglobin, serum hemolysis value, hemolysis index, icterus value (Iv), icterus index (Ii), lipemia value (Lv), and lipemia index. To conduct feature selection and identify the optimal combination of variables, linear regression machine learning was performed 1,000 times. ResultsThe selected variables were total bilirubin, direct bilirubin, total protein, albumin, hemoglobin, Iv, Ii, and Lv. The best predictive performance for high delta bilirubin concentrations was achieved with the combination of albumin-direct bilirubin-hemoglobin-Iv-Lv. The final equation composed of these variables was as follows: delta bilirubin = 0.35 × Iv + 0.05 × Lv − 0.23 × direct bilirubin − 0.05 × hemoglobin − 0.04 × albumin + 0.10. ConclusionThe equation established in this study is practical and can be easily applied in real-time in clinical laboratories.

Feature selection in peer-to-peer lending based on hybrid modified grey wolf optimization with optimized decision tree for credit risk assessment

ABSTRACT Lending platforms operating on a peer-to-peer (P2P) basis encounter the intricate challenge of assessing borrower creditworthiness to minimize the risk of defaults. This study addresses this challenge by proposing an advanced approach to feature selection that leverages the Grey Wolf Optimizer (GWO) in conjunction with a finely tuned Decision Tree (DT) model. The main objective is to enhance the precision and efficiency of feature selection processes within P2P lending datasets. The study begins by fine-tuning DT hyperparameters using Genetic Algorithms (GA), yielding an optimal configuration: ‘max_depth’ = 40, ‘min_samples_leaf’ = 20, and ‘criterion’ = ‘entropy’. Subsequent phases involve the application of GWO and modified GWO (nGWO, cGWO, and lGWO) to conduct feature selection under distinct Search Agent (SA) setups (SA = 5, SA = 20, SA = 50). Particularly impressive is the performance of the lGWO model with the SA = 50 setup, achieving a remarkable 91% accuracy while selecting 80.55% of the total 36 features. This study significantly improves how lenders manage risks in P2P lending by identifying high-risk borrowers more effectively, helping lenders reduce financial risks and benefiting all parties involved.

Fast Sparse Discriminative K-Means for Unsupervised Feature Selection.

Embedded feature selection approach guides subsequent projection matrix (selection matrix) learning through the acquisition of pseudolabel matrix to conduct feature selection tasks. Yet the continuous pseudolabel matrix learned from relaxed problem based on spectral analysis deviates from reality to some extent. To cope with this issue, we design an efficient feature selection framework inspired by classical least-squares regression (LSR) and discriminative K-means (DisK-means), which is called the fast sparse discriminative K-means (FSDK) for the feature selection method. First, the weighted pseudolabel matrix with discrete trait is introduced to avoid trivial solution from unsupervised LSR. On this condition, any constraint imposed into pseudolabel matrix and selection matrix is dispensable, which is significantly beneficial to simplify the combinational optimization problem. Second, the l2,p -norm regularizer is introduced to satisfy the row sparsity of selection matrix with flexible p . Consequently, the proposed FSDK model can be treated as a novel feature selection framework integrated from the DisK-means algorithm and l2,p -norm regularizer to optimize the sparse regression problem. Moreover, our model is linearly correlated with the number of samples, which is speedy to handle the large-scale data. Comprehensive tests on various data terminally illuminate the effectiveness and efficiency of FSDK.

An improved empirical mode decomposition method with ensemble classifiers for analysis of multichannel EEG in BCI emotion recognition

Emotion recognition using EEG is a difficult study because the signals’ unstable behavior, which is brought on by the brain’s complex neuronal activity, makes it difficult to extract the underlying patterns inside it. Therefore, to analyse the signal more efficiently, in this article, a hybrid model based on IEMD-KW-Ens (Improved Empirical Mode Decomposition-Kruskal Wallis-Ensemble classifiers) technique is used. Here IEMD based technique is proposed to interpret EEG signals by adding an improved sifting stopping criterion with median filter to get the optimal decomposed EEG signals for further processing. A mixture of time, frequency and non-linear distinct features are extracted for constructing the feature vector. Afterward, we conducted feature selection using KW test to remove the insignificant ones from the feature set. Later the classification of emotions in three-dimensional model is performed in two categories i.e. machine learning based RUSBoosted trees and deep learning based convolutional neural network (CNN) for DEAP and DREAMER datasets and the outcomes are evaluated for valence, arousal, and dominance classes. The findings demonstrate that the hybrid model can successfully classify emotions in multichannel EEG signals. The decomposition approach is also instructive for improving the model’s utility in emotional computing.

Swing-phase detection of locomotive mode transitions for smooth multi-functional robotic lower-limb prosthesis control.

Robotic lower-limb prostheses, with their actively powered joints, may significantly improve amputee users' mobility and enable them to obtain healthy-like gait in various modes of locomotion in daily life. However, timely recognition of the amputee users' locomotive mode and mode transition still remains a major challenge in robotic lower-limb prosthesis control. In the paper, the authors present a new multi-dimensional dynamic time warping (mDTW)-based intent recognizer to provide high-accuracy recognition of the locomotion mode/mode transition sufficiently early in the swing phase, such that the prosthesis' joint-level motion controller can operate in the correct locomotive mode and assist the user to complete the desired (and often power-demanding) motion in the stance phase. To support the intent recognizer development, the authors conducted a multi-modal gait data collection study to obtain the related sensor signal data in various modes of locomotion. The collected data were then segmented into individual cycles, generating the templates used in the mDTW classifier. Considering the large number of sensor signals available, we conducted feature selection to identify the most useful sensor signals as the input to the mDTW classifier. We also augmented the standard mDTW algorithm with a voting mechanism to make full use of the data generated from the multiple subjects. To validate the proposed intent recognizer, we characterized its performance using the data cumulated at different percentages of progression into the gait cycle (starting from the beginning of the swing phase). It was shown that the mDTW classifier was able to recognize three locomotive mode/mode transitions (walking, walking to stair climbing, and walking to stair descending) with 99.08% accuracy at 30% progression into the gait cycle, well before the stance phase starts. With its high performance, low computational load, and easy personalization (through individual template generation), the proposed mDTW intent recognizer may become a highly useful building block of a prosthesis control system to facilitate the robotic prostheses' real-world use among lower-limb amputees.

Wild Horse Optimizer and Support Vector Machine (SVM) Classifier Predicts The Heart Disease Converging Nature-Motivated Optimization and Machine Learning

Background: As one of the leading causes of morbidity and mortality worldwide, the diagnosis of acute cardiovascular disease (acute CVD) in emergency settings remains a significant challenge. Machine learning (ML) is artificial intelligence that allows computer programs to learn from and analyze large data sets without human intervention. As a diagnostic tool, ML presents numerous advantages, such as convenience, speed, and affordability. Methods: This research utilized a new model for predicting heart disease for feature selection(Wild Horse Optimizer, WHO) and another classification model (Support Vector Machine, SVM). In the WHO algorithm, like envisioning social behaviors of wild horses, we observe that wild horses display diverse behaviors like leading, grazing, moving, mating and chase. One odd behavior about wild horses is that young foals will break away from the herd to avoid the breed in its related herd before reaching maturity. It defines the decencies of the horses. Discussion: Multiple ML techniques were employed to train classifiers using the Cleveland heart disease dataset after conducting feature selection using WHO algorithm. The performance of these models was evaluated based on different parameters like Sensitivity, Accuracy, Specificity, and AUC. The SVM classifier model trained using the WHO approach proved better than the existing methods. In the research, we proposed a Wild Horse Optimization algorithm to optimize the features for Heart Disease prediction. After feature optimization, the optimized dataset is then given to Support Vector machine classification, which performs the classification of heart disease. After evaluating different experimental results, authors conclude that combining this feature selection algorithm and SVM as a classifier for forecasting heart disease is very effective. With the help of this approach, we can improve the early detection of heart disease and effectively manage severe heart disease. The experimental result graph shows an increased accuracy rate of the spectra feature subject.

Driver state recognition with physiological signals: Based on deep feature fusion and feature selection techniques

Driver state constitutes a significant factor affecting traffic safety, and accurate detection of driver state can significantly enhance driving safety. Therefore, the objective of this investigation is to develop a high accuracy driver state recognition model driven by physiological data. Firstly, expert knowledge and deep learning algorithms were used to extract the shallow and deep features of the driver's electroencephalogram (EEG) and electrodermal activity (EDA) data, respectively; secondly, the principal component analysis (PCA) technique was employed to reduce dimensionality of deep features, enabling fusion with the shallow features; thirdly, an improved recursive feature elimination (RFE) algorithm based on Shapley additive explanation (SHAP) value was proposed for the issue of redundant features in the fused features, and a hybrid feature selection algorithm was devised in conjunction with the chi-square test; finally, the driver state was recognized based on machine learning algorithms. The findings demonstrate superior performance of the driver state recognition model built on the fused features compared to models constructed solely with shallow or deep features. Moreover, the application of the hybrid feature selection algorithm further improved the performance of the driver state recognition model, resulting in a satisfactory accuracy (94.88 %) and F1 score (94.86 %). This study highlights the efficacy of fusing deep features and conducting feature selection in enhancing the accuracy of the driver state recognition model.

Robust and Sparse Principal Component Analysis With Adaptive Loss Minimization for Feature Selection.

Principal component analysis (PCA) is one of the most successful unsupervised subspace learning methods and has been used in many practical applications. To deal with the outliers in real-world data, robust principal analysis models based on various measure are proposed. However, conventional PCA models can only transform features to unknown subspace for dimensionality reduction and cannot perform features' selection task. In this article, we propose a novel robust PCA (RPCA) model to mitigate the impact of outliers and conduct feature selection, simultaneously. First, we adopt σ -norm as reconstruction error (RE), which plays an important role in robust reconstruction. Second, to conduct feature selection task, we apply l2,0 -norm constraint to subspace projection. Furthermore, an efficient iterative optimization algorithm is proposed to solve the objective function with nonconvex and nonsmooth constraint. Extensive experiments conducted on several real-world datasets demonstrate the effectiveness and superiority of the proposed feature selection model.

Toward a decision-making system based on artificial intelligence for precision marketing: A case study of Morocco

The insurance sector plays a crucial role in fostering sustainable economic development within a country. As the customer base grows, insurance companies must prioritize transitioning to data-driven strategies to cut costs and make more informed marketing choices in today’s digital era. This study proposes a new decision-making framework for precision marketing, based on a real case study from a Moroccan insurance company that aims to solve a practical problem. The proposed decision-making system consists of four components, with each component involving important steps. Firstly, data preparation was performed, consisting of four critical stages: data acquisition, data cleaning and filtering, feature selection, and oversampling. Secondly, top 20% and top 50% consumers are taken as examples to present their customer persona in detail. Based on the processed data, we analysed consumer consumption behaviors using four ML algorithms and made a performance comparison of the four algorithms. Additionally, we conducted feature selection methods to identify the most relevant features and evaluate the system’s performance. The aim of the proposed precision decision-making system is to assist managers in discerning the distinctive characteristics of potential customers and proposing tailored precision marketing strategies. This approach is expected to substantially reduce advertising expenses and enhance overall marketing efficiency. A case study using real-world data from a Moroccan insurance company was conducted to demonstrate the practical implementation of the proposed framework. The results of the study indicate that the proposed system yielded favourable outcomes.

Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.

We aimed to develop, train, and validate machine learning models for predicting preterm birth (<37 weeks' gestation) in singleton pregnancies at different gestational intervals. Models were developed based on complete data from 22,603 singleton pregnancies from a prospective population-based cohort study that was conducted in 51 midwifery clinics and hospitals in Wenzhou City of China between 2014 and 2016. We applied Catboost, Random Forest, Stacked Model, Deep Neural Networks (DNN), and Support Vector Machine (SVM) algorithms, as well as logistic regression, to conduct feature selection and predictive modeling. Feature selection was implemented based on permutation-based feature importance lists derived from the machine learning models including all features, using a balanced training data set. To develop prediction models, the top 10%, 25%, and 50% most important predictive features were selected. Prediction models were developed with the training data set with 5-fold cross-validation for internal validation. Model performance was assessed using area under the receiver operating curve (AUC) values. The CatBoost-based prediction model after 26 weeks' gestation performed best with an AUC value of 0.70 (0.67, 0.73), accuracy of 0.81, sensitivity of 0.47, and specificity of 0.83. Number of antenatal care visits before 24 weeks' gestation, aspartate aminotransferase level at registration, symphysis fundal height, maternal weight, abdominal circumference, and blood pressure emerged as strong predictors after 26 completed weeks. The application of machine learning on pregnancy surveillance data is a promising approach to predict preterm birth and we identified several modifiable antenatal predictors.

Prediction and feature analysis of breast cancer based on machine learning technology

Breast cancer, whose incidence rate is increasing year by year, is one of the malignant tumours with the highest incidence rate in women. Every year, an increment of about 1300000 people suffers from breast cancer and 400000 people die from it globally. For the sake of those who may be at risk of breast cancer, it is of critical importance to establish a model that can make predictions of breast cancer. This study utilizes the Random Forest algorithm and Logistic Regression algorithm to construct an analysis model. The study is conducted on a breast cancer dataset that contains data derived from Wisconsin. Specifically, the research conducts feature selection and manages to work out the relationship between various features and tumour types and selects the 5 most significant features. Based on the data of those 5 features, the accuracy of the two models is compared and the Logistic Regression Model is further optimized to reach a higher prediction accuracy. This study is highly significant in the medical community since the model it created can help with breast cancer prediction, allowing for early intervention and a higher survival rate for possible breast cancer patients.

Machine learning-based prediction of the outcomes of cochlear implantation in patients with inner ear malformation.

The objectives of this study are twofold: first, to visualize the structure of malformed cochleae through image reconstruction; and second, to develop a predictive model for postoperative outcomes of cochlear implantation (CI) in patients diagnosed with cochlear hypoplasia (CH) and incomplete partition (IP) malformation. The clinical data from patients diagnosed with cochlear hypoplasia (CH) and incomplete partition (IP) malformation who underwent cochlear implantation (CI) at Beijing Tongren Hospital between January 2016 and August 2020 were collected. Radiological features were analyzed through 3D segmentation of the cochlea. Postoperative auditory speech rehabilitation outcomes were evaluated using the Categories of Auditory Performance (CAP) and the Speech Intelligibility Rating (SIR). This study aimed to investigate the relationship between cochlear parameters and postoperative outcomes. Additionally, a predictive model for postoperative outcomes was developed using the K-nearest neighbors (KNN) algorithm. In our study, we conducted feature selection by using patients' imaging and audiological attributes. This process involved methods such as the removal of missing values, correlation analysis, and chi-square tests. The findings indicated that two specific features, cochlear volume (V) and cochlear canal length (CDL), significantly contributed to predicting the outcomes of hearing and speech rehabilitation for patients with inner ear malformations. In terms of hearing rehabilitation, the KNN classification achieved an accuracy of 93.3%. Likewise, for speech rehabilitation, the KNN classification demonstrated an accuracy of 86.7%. The measurements obtained from the 3D reconstruction model hold significant clinical relevance. Despite the considerable variability in cochlear morphology across individuals, radiological features remain effective in predicting cochlear implantation (CI) prognosis for patients with inner ear malformations. The utilization of 3D segmentation techniques and the developed predictive model can assist surgeons in conducting preoperative cochlear structural measurements for patients with inner ear malformations. This, in turn, can offer a more informed perspective on the anticipated outcomes of cochlear implantation.

Lung adenocarcinoma identification based on hybrid feature selections and attentional convolutional neural networks.

Lung adenocarcinoma, a chronic non-small cell lung cancer, needs to be detected early. Tumor gene expression data analysis is effective for early detection, yet its challenges lie in a small sample size, high dimensionality, and multi-noise characteristics. In this study, we propose a lung adenocarcinoma convolutional neural network (LATCNN), a deep learning model tailored for accurate lung adenocarcinoma prediction and identification of key genes. During the feature selection stage, we introduce a hybrid algorithm. Initially, the fast correlation-based filter (FCBF) algorithm swiftly filters out irrelevant features, followed by applying the k-means-synthetic minority over-sampling technique (k-means-SMOTE) method to address category imbalance. Subsequently, we enhance the particle swarm optimization (PSO) algorithm by incorporating fast-decay dynamic inertia weights and utilizing the classification and regression tree (CART) as the fitness function for the second stage of feature selection, aiming to further eliminate redundant features. In the classifier construction stage, we present an attention convolutional neural network (atCNN) that incorporates an attention mechanism. This improved model conducts feature selection post lung adenocarcinoma gene expression data analysis for classification and prediction. The results show that LATCNN effectively reduces the feature dimensions and accurately identifies 12 key genes with accuracy, recall, F1 score, and MCC of 99.70%, 99.33%, 99.98%, and 98.67%, respectively. These performance metrics surpass those of other comparative models, highlighting the significance of this research for advancing lung adenocarcinoma treatment.

A mixture distributions analysis based feature selection approach for bearing remaining useful life estimation

Feature selection is a difficult but highly important preliminary step for bearings remaining useful life (RUL) estimation. To avoid the weights setting problem in hybrid metric, this work devotes to conduct feature selection by using a single metric. Due to noise and outliers, an existing feature selection metric, called monotonicity, used for estimating bearings RUL, requires data smoothing processing before adequate implementation. Such a smoothing process may remove significant part of meaningful information from data. To overcome this issue, a mixture distribution analysis-based feature selection metric is proposed. Moreover, based on this new metric, a feature selection approach for bearings RUL estimation is proposed. Numerical experiments benchmarking the proposed method and the existing metric monotonicity method on available real datasets highlight its effectiveness.

CSCIM_FS: Cosine similarity coefficient and information measurement criterion-based feature selection method for high-dimensional data

Feature selection (FS) based on mutual information (MI) metrics needs to discretize the data in preprocessing, which is a convenient way to identify correlation between features. However, information loss often occurs in data discretization. In order to solve this information loss problem, this paper proposes a FS algorithm based on cosine similarity coefficient and information measurement criterion (CSCIM_FS). First, the MI between features and tags is calculated, and features are sorted out according to the MI calculated. Then, a feature matrix is constructed to transform the one-dimensional feature sequence into a two-dimensional square matrix. Next, cosine transform is adopted to obtain the high-frequency components of the feature matrix, and sampling is conducted to derive the hash fingerprint of the feature matrix. After that, the similarity between every two features is calculated on the basis of the hash fingerprints of different features. Finally, the feature weight is calculated according to tags, the MI and similarity between features, and a key feature subset is obtained and used to conduct feature selection from the data. The experimental results on several UCI public datasets show that CSCIM_FS algorithm selected a feature subset with high accuracy, and that this algorithm performs better than MIM, CMIM, mRMR and other algorithms.

Sentimental Analysis on Webio Comments using Machine Learning

Sentiment analysis is a powerful technique that allows us to automatically classify the sentiment of a text into positive, negative or neutral categories. In this study, we explore the application of sentiment analysis on Webio comments using machine learning algorithms. Webio is a popular online platform that enables users to interact with chatbots and customer service representatives. By analysing the sentiment of Webio comments, businesses can gain value insights into customer satisfaction and identify areas of improvement in their products or services. We used a dataset of Webio comments and employed various machine learning algorithms, including Support Vector Machines(SVM), Naive Bayes(NB), and Random Forest(RF), to classify the sentiment of the comments. We evaluated the performance of the algorithms using metrics such as accuracy, precision, recall, and F1-score. Our results show that the SVM algorithm outperformed the algorithms, achieving an accuracy of 85% in classifying the sentiment of Webio comments. We also conducted feature selection experiments to identify the most important features in the classification task, and found that the presence of certain keywords and emoticons had a significant impact on sentiment classification. Overall, our study demonstrates the effectiveness of machine learning algorithms in analysing sentiment on Webio comments and provides insights into the features that are most indicative of sentiment in this context

Double groups of gates based Takagi-Sugeno-Kang (DG-TSK) fuzzy system for simultaneous feature selection and rule extraction

Embedded feature selection methods based on fuzzy model have been well studied, in which the gate functions are usually used to distinguish the essential features. Fuzzy rules play a critical role in constructing fuzzy systems. However, few works simultaneously evaluate the quality of fuzzy rules during the fuzzy system based feature selection approaches. This article proposes double groups of gates based Takagi-Sugeno-Kang (DG-TSK) fuzzy model to simultaneously conduct feature selection and rule extraction in one training phase. Firstly, a novel gate function is introduced, which is embedded into the system. We call this function as M-gate since it is shaped like an M. Secondly, a new type of fuzzy rule base (FRB) is designed by transforming each data point into one fuzzy rule, which is named point-based FRB (P-FRB). Based on the M-gate and P-FRB, DG-TSK model is developed, in which M-gates are embedded in the antecedents for feature selection and consequents for rule extraction. Note that these two procedures are performed at the same time. We test DG-TSK on 18 classification datasets and compare it with other similar methods. The experiment results demonstrate that DG-TSK is effective.

Data-Driven Algorithms for Image-Based Rock Classification and Formation Evaluation in Formations With Rapid Spatial Variation in Rock Fabric

Supervised learning algorithms can be employed for the automation of time-intensive tasks, such as image-based rock classification. However, labeled data are not always available. Alternatively, unsupervised learning algorithms, which do not require labeled data, can be employed. Using either of these methods depends on the evaluated formations and the available training/input data sets. Therefore, further investigation is needed to compare the performance of both approaches. The objectives of this paper are (a) to train two supervised learning models for image-based rock classification employing image-based features from computerized tomography (CT) scan images and core photos, (b) to conduct image-based rock classification using the trained model, (c) to compare the results obtained using supervised learning models against an unsupervised learning-based workflow for rock classification, and (d) to derive class-based petrophysical models for improved estimation of petrophysical properties First, we removed non-formation visual elements from the core image data, such as induced fractures, the core barrel, and the seal peel tag on core photos. Then, we computed image-based features such as grayscale, color, and textural features from core image data and conducted feature selection. Then, we employed the extracted features for model training. Finally, we used the trained model to conduct rock classification and compared the obtained rock classes against the results obtained from an unsupervised image-based rock classification workflow. This workflow uses image-based rock fabric features coupled with a physics-based cost function for the optimization of rock classes. We applied the workflow to one well intersecting three formations with rapid spatial variation in rock fabric. We used 60% of the data to train a random forest and a support vector machines classifier using a 5-fold cross-validation approach. The remaining 40% of the data was used to test the accuracy of the supervised models. We established a base case of unsupervised learning rock classification and four different cases of supervised learning rock classification. The highest accuracy obtained for supervised rock classification was 97.4%. The accuracy obtained in the unsupervised learning rock classification approach was 82.7% when compared against expert-derived lithofacies. Class-based permeability estimates decreased the mean relative error by 34% and 35% when compared with formation-based permeability estimates, for the supervised and unsupervised approaches, respectively. The highest accuracies for the supervised and unsupervised models were obtained when integrating features from CT-scan images and core photos, highlighting the importance of feature selection for machine-learning workflows. A comparison of the two approaches for rock classification showed higher accuracy obtained from the supervised learning approach. However, the unsupervised method provided reasonable accuracy as well as a more general and faster approach for rock classification and enhanced formation evaluation.