Minimum Redundancy Maximum Relevance Feature Selection Research Articles

Data-driven learning-based classification model for mitigating false data injection attacks on dynamic line rating systems

The increasing need to explore electric power grid expansion technologies like dynamic line rating (DLR) systems and their dependence on real-time weather data for system planning necessitates research into false data injection attacks (FDIA) on cyber-physical power systems (CPS). This study aims to develop a robust machine learning model to mitigate FDIA in DLR systems, focusing on statistical data processing, feature ranking, selection, training, validation and evaluation. It synthesises z-score and other statistical analyses with minimum redundancy maximum relevance (MR-MR) feature ranking and selection algorithm to improve model performance and generalisation of binary generalised linear model logistic regression (BGLM-LR) and other machine learning classification algorithms. The resulting models formed with BGLM-LR, Gaussian naïve Bayes (GNB), linear support vector machine (LSVM), wide neural network (WNN), and decision tree (DT) were trained and tested with 10-year hourly DLR history data features. The evaluation of the models on unseen data revealed enhanced validation and testing accuracies after the MR-MR feature ranking and selection. BGLM-LR, GNB, LSVM, and WNN showed promising performance for mitigating FDIA. However, the study identifies DT’s limitations as overfitting and lacking generalisation in FDIA mitigation. z-score-MR-MR-BGLM-LR and z-score-MR-MR-LSVM models exhibited outstanding performances with zero false negative rates highlighting the significance of feature ranking and selection. Still, the z-score-MR-MR-BGLM-LR combination exhibits the highest marginal improvement from training to testing, the lowest training and validation time and a perfect area under curve (AUC) of the receiver operating characteristics making it the best choice in mitigating FDIA when computational resources are limited.

Accurate Liver Fibrosis Detection Through Hybrid MRMR-BiLSTM-CNN Architecture with Histogram Equalization and Optimization.

The early detection and accurate diagnosis of liver fibrosis, a progressive and potentially serious liver condition, are crucial for effective medical intervention. Invasive methods like biopsies for diagnosis can be risky and expensive. This research presents a novel computer-aided diagnosis model for liver fibrosis using a hybrid approach of minimum redundancy maximum relevance (MRMR) feature selection, bidirectional long short-term memory (BiLSTM), and convolutional neural networks (CNN). The proposed model involves multiple stages, including image acquisition, preprocessing, feature representation, fibrous tissue identification, and classification. Notably, histogram equalization is employed to enhance image quality by addressing variations in brightness levels. Performance evaluation encompasses a range of metrics such as accuracy, precision, sensitivity, specificity, F1 score, and error rate. Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with small datasets. The model tackles the complexities of hyperparameter optimization through the IHO algorithm and reduces training time by leveraging MRMR feature selection. In practical application, the proposed hybrid MRMR-BiLSTM-CNN method demonstrates remarkable performance with a 97.8% accuracy rate in identifying liver fibrosis images. It exhibits high precision, sensitivity, specificity, and minimal error rate, showcasing its potential for accurate and non-invasive diagnosis.

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study

Purpose. This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients. Methods. A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity. Results. A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5–1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5–2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT. Conclusion. Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.

Identifying Genetic Signatures from Single-Cell RNA Sequencing Data by Matrix Imputation and Reduced Set Gene Clustering

In this current era, the identification of both known and novel cell types, the representation of cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in various cells are the key areas of interest in the analysis of single-cell RNA sequencing (scRNA-seq) data. Due to the nature of the data, cluster identification in single-cell sequencing data with high dimensions presents several difficulties. In this paper, we introduce a new framework that combines various strategies such as imputed matrix, minimum redundancy maximum relevance (MRMR) feature selection, and shrinkage clustering to discover gene signatures from scRNA-seq data. Firstly, we conducted the pre-filtering of the “drop-out” value in the data focusing solely on imputing the identified “drop-out” values. Next, we applied the MRMR feature selection method to the imputed data and obtained the top 100 features based on the MRMR feature selection optimization scores for further downstream analysis. Thereafter, we employed shrinkage clustering on the selected feature matrix to identify the cell clusters using a global optimization approach. Finally, we applied the Limma-Voom R tool employing voom normalization and an empirical Bayes test to detect differentially expressed features with a false discovery rate (FDR) < 0.001. In addition, we performed the KEGG pathway and gene ontology enrichment analysis of the identified biomarkers using David 6.8 software. Furthermore, we conducted miRNA target detection for the top gene markers and performed miRNA target gene interaction network analysis using the Cytoscape online tool. Subsequently, we compared our detected 100 markers with our previously detected top 100 cluster-specified markers ranked by FDR of the latest published article and discovered three common markers; namely, Cyp2b10, Mt1, Alpi, along with 97 novel markers. In addition, the Gene Set Enrichment Analysis (GSEA) of both marker sets also yields similar outcomes. Apart from this, we performed another comparative study with another published method, demonstrating that our model detects more significant markers than that model. To assess the efficiency of our framework, we apply it to another dataset and identify 20 strongly significant up-regulated markers. Additionally, we perform a comparative study of different imputation methods and include an ablation study to prove that every key phase of our framework is essential and strongly recommended. In summary, our proposed integrated framework efficiently discovers differentially expressed stronger gene signatures as well as up-regulated markers in single-cell RNA sequencing data.

A machine learning-based sonomics for prediction of thyroid nodule malignancies.

This study aims to use ultrasound derived features as biomarkers to assess the malignancy of thyroid nodules in patients who were candidates for FNA according to the ACR TI-RADS guidelines. Two hundred and ten patients who met the selection criteria were enrolled in the study and subjected to ultrasound-guided FNA of thyroid nodules. Different radiomics features were extracted from sonographic images, including intensity, shape, and texture feature sets. Least Absolute Shrinkage and Selection Operator (LASSO), Minimum Redundancy Maximum Relevance (MRMR), and Random Forests/Extreme Gradient Boosting Machine (XGBoost) algorithms were used for feature selection and classification of the univariate and multivariate modeling, respectively. Evaluation of models performed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). In the univariate analysis, Gray Level Run Length Matrix - Run-Length Non-Uniformity (GLRLM-RLNU) and gray-level zone length matrix - Run-Length Non-Uniformity (GLZLM-GLNU) (both with an AUC of 0.67) were top-performing for predicting nodules malignancy. In the multivariate analysis of the training dataset, the AUC of all combinations of feature selection algorithms and classifiers was 0.99, and the highest sensitivity was for XGBoost classifier and MRMR feature selection algorithms (0.99). Finally, the test dataset was used to evaluate our model in which XGBoost classifier with MRMR and LASSO feature selection algorithms had the highest performance (AUC = 0.95). Ultrasound-extracted features can be used as non-invasive biomarkers for thyroid nodules' malignancy prediction.

Recognition of DDoS attacks on SD-VANET based on combination of hyperparameter optimization and feature selection

The aim of Vehicular Ad Hoc Networks (VANETs) is to provide drivers and passengers with various applications and services for comfortable transportation by supporting traffic efficiency and safety. However, the traditional VANETs face various technical challenges in meeting the basic requirements of intelligent transportation systems such as scalability, flexibility and management due to the ever-increasing number of intelligent vehicles. With its flexible, programmable, scalable network structure, Software Defined Networks (SDNs) are candidates for providing solutions to the problems experienced. The architecture, which was created by adapting the SDN paradigm to the traditional VANET is simply called SD-VANET. This new architecture allows easy scaling of the network and flexible network management. Despite the advantages of SD-VANET architecture, it is also vulnerable to cyberattack threats such as Distributed Denial Of Service (DDoS). In this study, different machine learning classifiers were used to detect DDoS attacks targeting SD-VANETs. First, a dataset containing features of normal network traffic and DDoS attack network traffic was obtained from an experimentally created SD-VANET topology. Then the Minimum Redundancy Maximum Relevance (MRMR) feature selection algorithm was used to select the most distinctive features of the dataset. Machine learning classifiers were trained and tested with both original and feature selection applied datasets. Moreover, in the learning phase, hyperparameter optimization for the classifiers was applied using the Bayesian optimization method. According to the experimental results, the highest accuracy score obtained was 99.35% with MRMR feature selection and Bayesian optimization-based decision tree classifier. The results demonstrate that the MRMR feature selection and Bayesian optimization-based classifier approach have been successful for the detection of DDoS attacks on SD-VANETs.

Integration of transcriptomic data identifies key hallmark genes in hypertrophic cardiomyopathy

BackgroundHypertrophic cardiomyopathy (HCM) represents one of the most common inherited heart diseases. To identify key molecules involved in the development of HCM, gene expression patterns of the heart tissue samples in HCM patients from multiple microarray and RNA-seq platforms were investigated.MethodsThe significant genes were obtained through the intersection of two gene sets, corresponding to the identified differentially expressed genes (DEGs) within the microarray data and within the RNA-Seq data. Those genes were further ranked using minimum-Redundancy Maximum-Relevance feature selection algorithm. Moreover, the genes were assessed by three different machine learning methods for classification, including support vector machines, random forest and k-Nearest Neighbor.ResultsOutstanding results were achieved by taking exclusively the top eight genes of the ranking into consideration. Since the eight genes were identified as candidate HCM hallmark genes, the interactions between them and known HCM disease genes were explored through the protein–protein interaction (PPI) network. Most candidate HCM hallmark genes were found to have direct or indirect interactions with known HCM diseases genes in the PPI network, particularly the hub genes JAK2 and GADD45A.ConclusionsThis study highlights the transcriptomic data integration, in combination with machine learning methods, in providing insight into the key hallmark genes in the genetic etiology of HCM.

The SVM Binary Tree Classification Using MRMR and F-Score Feature Selection Algorithms

The discrimination between various types of speech and non-speech signals in audio data stream is the fundamental step for further indexing and retrieving. This paper considers some of the basic problems in audio content classification which is the key component in automatic audio retrieval system. It illustrates a potential use of statistical learning algorithm called support vector machine (SVM) for broadcast news (BN) audio classification task. The overall classification architecture uses binary tree SVM (BT-SVM) decision scheme in combination with well known audio features such as, MFCCs and low level MPEG-7 audio descriptors. The important step in creating such classification system is to define the optimal features for each binary SVM classifier. There exist various feature selection algorithms that help to create such feature set. Therefore we decided to implement F-score and Minimum Redundancy Maximum Relevance (MRMR) feature selection algorithms, as an effective search algorithms used in many pattern recognition tasks.

Evolving feature selection

Data preprocessing is an indispensable step in effective data analysis. It prepares data for data mining and machine learning, which aim to turn data into business intelligence or knowledge. Feature selection is a preprocessing technique commonly used on high-dimensional data. Feature selection studies how to select a subset or list of attributes or variables that are used to construct models describing data. Its purposes include reducing dimensionality, removing irrelevant and redundant features, reducing the amount of data needed for learning, improving algorithms' predictive accuracy, and increasing the constructed models' comprehensibility. This article considers feature-selection overfitting with small-sample classifier design; feature selection for unlabeled data; variable selection using ensemble methods; minimum redundancy-maximum relevance feature selection; and biological relevance in feature selection for microarray data.