Optimal Feature Subset Selection Research Articles

Predicting creep life of CrMo pressure vessel steel using machine learning models with optimal feature subset selection

The data-driven approach for creep life prediction typically integrates numerous characteristics, including material compositions, manufacturing details, and service conditions, into machine learning models. In this study, a machine learning-based creep life prediction approach with optimal feature subset selection is established for 2.25Cr1Mo pressure vessel steel. Before model training and testing, six critical features that significantly impact the creep life of 2.25Cr1Mo steel are selected, specifically the applied stress, temperature, and chemical compositions consisting of Cr, Ni, Mn, and Mo. Various machine learning algorithms, along with the traditional L–M method, are utilized for model training and performance evaluation. Additionally, the developed models undergo validation using experimental data independent of the training and testing datasets to assess their generalization abilities. The results reveal that, among all tested models, the support vector regression (SVR) model, coupled with the optimal feature subset, demonstrates superior prediction accuracy and generalization capability. Finally, the creep life prediction model exhibiting optimal performance is deployed into a software application, leveraging the Python programming language. This predictor tool facilitates rapid and precise creep life predictions for 2.25Cr1Mo pressure vessel steel, relying solely on a limited amount of input information, and provides a clear and visual presentation of the prediction results.

An optimal feature subset selection technique to improve accounting information security for intrusion detection systems

An optimal feature subset selection technique to improve accounting information security for intrusion detection systems

Optimal feature subset selection for MRI brain tumor classification using improved ant-lion optimization

Optimal feature subset selection for MRI brain tumor classification using improved ant-lion optimization

Corroded submarine pipeline degradation prediction based on theory-guided IMOSOA-EL model

Submarine pipelines play an important role in the oil and gas transportation. However, pipeline corrosion can cause the structural degradation of pipelines, which may lead to failure accident and environmental pollution. Pipeline degradation prediction based on monitoring data is of great significance for preventing corrosion failure. In this paper, an ensemble learning (EL) based approach integrated with multi-objective optimization is developed for pipeline corrosion degradation prediction. Firstly, the engineering theory and domain knowledge are integrated into feature engineering to improve the interpretability. Several new feature variables are constructed based on the corrosion mechanism and empirical models. Secondly, an improved multi-objective seagull optimization algorithm (IMOSOA) is proposed to optimize the hyper-parameters of EL model. Subsequently, a novel data-driven model, so-called theory-guided IMOSOA-EL is proposed for the corrosion rate prediction. And six benchmark functions are used to verify the optimization performance of IMOSOA. Then, different feature subsets are developed based on correlation analysis. A comprehensive evaluation indicator is proposed for the optimal feature subset selection. The results demonstrate that the proposed model presents superiority in prediction accuracy compared with other models. This study is significant for reliability assessment and maintenance decision-making of submarine pipelines.

Multi-Modal Feature Integration in Machine Learning Predictions for Cardiovascular Diseases

Early detection and prevention of cardiovascular illnesses rely heavily on phonocardiogram (PCG) and electrocardiogram (ECG). A novel multi-modal machine learning strategy based on ECG and PCG data is presented in this work for predicting cardiovascular diseases (CVD). ECG and PCG features are combined for optimal feature subset selection using a genetic algorithm (GA). Then, machine learning classifiers are implemented to do the classification of abnormal and normal signals

Filter-PCA-Based Process Monitoring and Defect Identification During Climbing Helium Arc Welding Process Using DE-SVM

In-process defect detection for climbing helium arc welding of aluminum alloy is challenging due to the highly complex nature of the heat and mass transfer process. This paper presents a process monitoring and defects identification method for weld quality monitoring during climbing helium arc welding based on molten pool visual sensing. High contrast molten pool images were acquired by the designed passive vision sensor. Then, an image processing algorithm based on Otsu's method and visual saliency features was proposed to extract multi-region features from the acquired images in real-time. An optimal feature subset selection approach based on filter and principal component analysis(Filter-PCA) was developed to select the components related to welding defects. Finally, a novel prediction model using Filter-PCA and the support vector machine based on differential evolution algorithm(DE-SVM) was established to identify undercut weld, snake-like weld, and sound weld. The experiment results obtained in the actual production line of 2219 aluminum alloy rocket propellent tank show that the proposed method is remarkably effective and robust. This research provides a technical approach for early identification and early warning of defects during climbing helium arc welding of aluminum alloy. It also provides a basis for stability and online control of weld formation quality under time-varying welding pose.

Multi-filter semi-supervised transformer model for fault diagnosis

Dissolved Gas Analysis (DGA) is the most commonly used method for power transformer fault diagnosis. However, very few reliable and labeled fault DGA samples are available in the transformer substation whilst DGA data without labels is easier to obtain, which makes it difficult to train fault detectors in high-dimensional input space or select features using wrapper methods. Therefore, in order to improve the fault diagnosis accuracy using limited labeled DGA samples but more unlabeled DGA data, this paper proposes a novel multi-filter semi-supervised feature selection method for selecting optimal DGA features and building effective fault diagnosis models. A confidence criterion is also proposed for selecting high confidence unlabeled data to expand the training data set. Five filter techniques based on different evaluation criteria are employed to rank input DGA features, and a feature combination method is then applied to aggregate feature ranks by multiple filters and form a lower-dimensional candidate feature subset. The proposed method has been tested by using the IEC T10 dataset and compared with traditional supervised diagnostic models. The results show that the proposed method works well in optimizing DGA features and improving fault diagnosis accuracy significantly. Besides, the robustness of the selection of optimal feature subset is validated by testing DGA samples from the local power utility.

An optimal model for classification of lung cancer using grey wolf optimizer and deep hybrid learning

In recent years, metaheuristic methods have shown major advantages in the field of feature selection due to its comprehensibility and possible extensive search competence. However, the majority of evolutionary computation-based feature selection algorithms in use today are wrapper approaches, which are expensive to compute, particularly for extensive biomedical data. Developing an effective evaluation strategy is crucial for significant reduction of computational cost. The proposed framework extracts deep feature from ResNet-50 and VGG-16 based convolutional neural models with initial segmentation process based on marker-controlled watershed method. Next the feature reduction is a two-fold approach with principal component analysis applied to reduce the dimensionality of large feature space from convolutional neural network (CNN) models as first step. The second step is optimal feature subset selection using a swarm intelligence method referred as modified grey wolf optimization. Finally, the selected feature subset is fed to various machine learning classifiers. The experimental result reveals that the proposed algorithm outperforms the other state-of-the-art methods with classification accuracy of 96.56%, thus upholding the dependability of the approach.

ETCD: An effective machine learning based technique for cardiac disease prediction with optimal feature subset selection

Cardiac disease is the leading cause of death worldwide. The early diagnosis and prognosis can help patients live longer by lowering mortality and boosting survival rates. The paucity of radiologists and doctors in various nations, due to a variety of factors, is a substantial barrier to early diagnosis. Computational intelligence is an emerging concept in the field of medical imaging to identify, prognosticate, and diagnose disease, among numerous initiatives to construct decision support systems. It relieves radiologists and doctors from being overworked and reduces the time it takes to diagnose patients promptly. In this work, an effective technique for cardiac disease (ETCD) prediction based on machine intelligence has been proposed. To ensure the success of our proposed model, we used effective Data Collection, Data Pre-processing, and feature selection process to generate accurate data for the training model. ETCD utilizes the optimal feature subset selection algorithm (OFSSA) to extract features from different datasets (Cleveland, Hungarian, Combined dataset, and Z_Alizadeh Saini datasets) having varying properties available at the UCI machine learning repository. With ETCD, the average accuracy performance for considered datasets gets increased with SVM, KNN, DT, NB, and RF classifiers by 6.227%, 2.72%, 7.345%, 14.084%, and 18.921% respectively. Further, the results of the experiments demonstrate that ETCD outperformed several contemporary baseline approaches in terms of accuracy and was comparable in terms of sensitivity, specificity, precision, and F_Score. ETCD returns the best feasible solution among all input predictive models considering performance criteria and improves the efficacy of the system, hence can assist doctors and radiologists in a better way to diagnose cardiac patients.

Identifying Coastal Wetlands Changes Using a High-Resolution Optical Images Feature Hierarchical Selection Method

Coastal wetlands are dynamic and fragile ecosystems where complex changes have taken place. As they are affected by environmental changes and human activities, it is of great practical significance to monitor coastal wetlands changes regularly. High-resolution optical data can observe changes in coastal wetlands, however, the impact of different optical features on the identification of changes in coastal wetlands is not clear. Simultaneously, the combination of many features could cause the “dimension disaster” problem. In addition, only small amounts of training samples are accessible at pre- or post-changed time. In order to solve the above problems, the feature hierarchical selection method is proposed, taking into account the jumping degree of different image features. The influence of different optical features on wetland classification was analyzed. In addition, a training samples transfer learning strategy was designed for wetland classification, and the classification result at pre- and post-changed times were compared to identify the “from-to” coastal wetlands changes. The southeastern coastal wetlands located in Jiangsu Province were used as a study area, and ZY-3 images in 2013 and 2018 were used to verify the proposed methods. The results show that the feature hierarchical selection method can provide a quantitative reference for optimal subset feature selection. A training samples transfer learning strategy was used to classify post-changed optical data, the overall accuracy of transferred training samples was 91.16%, and it ensures the accuracy requirements for change identification. In the study area, the salt marsh increased mainly from the sea area, because salt marshes expand rapidly throughout coastal areas, and aquaculture ponds increased from the sea area and salt marshes, because of the considerable economic benefits of the aquacultural industry.

Enhanced machine learning based feature subset through FFS enabled classification for cervical cancer diagnosis

A dataset that has massive features and imbalanced classes may be challenging for obtaining adequate accuracy in classification approaches of Machine Learning (ML). The purpose of this research is to find the optimal feature subset for cervical cancer diagnosis with efficient classification approach by estimating the performance of various Machine Learning predictive models. Filter-based feature selection techniques of Relief and Information Gain are applied in this study to calculate the rank for each feature that can be applied to order and select highest scoring features for feature selection. An optimal feature subset is generated with wrapper approach through Recursive Feature Elimination which uses a Random Forest procedure and Genetic Algorithm has been employed based on evolutionary principle. The predictive models are established with 10fold cross validation using prevalent classification algorithms like Random Forest, C5.0, K-Nearest Neighbour and Naïve Bayes. The results showed an enhancement in the average performance of these classifiers concurrently and the classification error for these classifiers decreases substantially. The experiments also exhibited that by employing this approach an optimal and reduced feature subset is desirable for the enrichment of classification accuracy with a lower computational cost. The features generated by fused approach of Relief and Genetic algorithm methods were able to predict the results in an efficient manner, hence an optimal feature subset has been nominated through this procedure. Maximum number of classifiers have shown good results in terms of performance outcomes. In addition, Random Forest method has shown advanced accuracy rate with an improved percentage of sensitivity and specificity results. Also, this work established that the best and optimal feature subset selection through Fused Feature Selection (FFS) approach could reduce the complexity of the predictive model.

MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks-CAN bus based on genetic algorithm and intrusion detection approach

Controller area network (CAN) bus which provides efficient, reliable and robust communication between electronic control units (ECUs) is the most frequently used protocol for in-vehicle networks. However, the lack of security mechanisms in the CAN protocol makes it vulnerable to inside and outside cyber-attacks. Therefore, an intrusion detection system (IDS), which is a widely used method to detect malicious activities, is preferred to improve the security of the CAN buses. In spite of the fact that various supervised and unsupervised machine learning algorithms are employed to increase the performance of IDSs, obtaining high classification performance is still a challenge for them. First, a lot of irrelevant and redundant features in the datasets result in long computational times with low detection performances. Second, different classification performances are acquired based on classifiers and a combination of the features. Third, many of the models suffer from unknown and different types of attacks. For these reasons, a new intrusion detection framework is proposed in this paper based on feature selection and classifier. Initially, we propose a meta-heuristic algorithm called modified genetic algorithm (MGA) m-feature selection for dimension reduction by selecting optimal feature subset based on k-fold cross validation. Then, we utilize five different linear and nonlinear classifiers: support vector classifier (SVC), logistic regression classifier (LRC), decision tree classifier (DTC), k-nearest neighbors classifier (KNC), and linear discriminant analysis classifier (LDAC) as candidate classifiers to develop an efficient IDS. Finally, we select the best classifier from the candidates and build an IDS. The experimental results reveal that the proposed MGA-DTC presents a better performance in terms of several metrics based on not only the HCRL-car hacking dataset but also UNSW-NB15, and CIC-IDS2017 datasets.

Distributed Evolutionary Feature Selection for Big Data Processing

Feature selection has become a powerful dimensional reduction strategy and an effective tool in handling high-dimensional data. Feature selection aims to reduce the dimension of the feature space, to speed up and reduce the cost of the learning model and that by selecting the most relevant feature subset to data mining and machine learning tasks. The selection of optimal feature subset is an optimization problem that proved to be NP-hard. Metaheuristics are traditionally used to deal with NP-hard problems since they are well known for solving complex and real-world problems in reasonable period of time. Genetic algorithm (GA) is one of the most popular metaheuristics algorithms, which proved to be effective for an accurate feature selection task. However, in the last few decades, data have become progressively larger in both numbers of instances and features. This paradigm is being popularly termed as Big Data. With the tremendous growth of dataset sizes, most current feature selection algorithms and exceptionally GA become unscalable. To improve the scalability of a feature selection algorithm on big data, the distributed computing strategy is always adopted such as MapReduce model and Hadoop system. In this paper, we first present a review for the most recent works which handle the use of Parallel Genetic algorithm in large datasets. Then, we will propose a new Parallel Genetic algorithm based on the Coarse-grained parallelization model (island model). The parallelization of the process and the distribution of the partitioning of data will be performed using Hadoop system with an Amazon cluster. The performance and the scalability of the proposed method were theoretically and empirically compared to the existing feature selection methods when handling large-scale datasets and results confirm the effectiveness of our proposed method.

An effective feature subset selection approach based on Jeffries-Matusita distance for multiclass problems

Jeffries-Matusita (JM) distance, a transformation of the Bhattacharyya distance, is a widely used measure of the spectral separability distance between the two class density functions and is generally used as a class separability measure. It can be considered to have good potential to be used for evaluation of the effectiveness of a feature in discriminating two classes. The capability of JM distance as a ranking based feature selection technique for binary classification problems has been verified in some research works as well as in our earlier work. It was found by our simulation experiments with benchmark data sets that JM distance works equally well compared to other popular feature ranking methods based on mutual information, information gain or Relief. Extension of JM distance measure for feature ranking in multiclass problems has also been reported in the literature. But all of them are basically rank based approaches which deliver the ranking of the features and do not automatically produce the final optimal feature subset. In this work, a novel heuristic approach for finding out the optimum feature subset from JM distance based ranked feature lists for multiclass problems have been developed without explicitly using any specific search technique. The proposed approach integrates the extension of JM measure for multiclass problems and the selection of the final optimal feature subset in a unified process. The performance of the proposed algorithm has been evaluated by simulation experiments with benchmark data sets in comparison with two other previously developed multiclass JM distance measures (weighted average JM distance and another multiclass extension equivalent to Bhattacharyya bound) and some other popular filter based feature ranking algorithms. It is found that the proposed algorithm performs better in terms of classification accuracy, F-measure, AUC with a reduced set of features and computational cost.

A novel TMGWO–SLBNC‐based multidimensional feature subset selection and classification framework for frequent diagnosis of breast lesion abnormalities

The selection of optimal subset of features from high-dimensional data sets still remains a major challenge during breast cancer detection and categorization. There exist several research works regarding optimal feature subset selection from high-dimensional data sets, but the obtained results are not satisfying when multidimensional data sets (MDDs) are employed in large amount during disease analysis. In this article, an effective feature subset selection and classification method suitable for MDD is proposed. At first, the important and distinct features are extracted from the mammogram images using a Deep Neural Network with wrapper-based extraction technique. Then, a novel two-phase mutation strategy integrated with grey wolf optimizer algorithm is employed for selecting the most relevant feature subsets. Finally, a learning-based semilazy Bayesian network classifier with parallel implementation is proposed for the precise categorization of the breast cancer stages. The proposed method is executed in MATLAB platform and analyzed using mammogram images taken from MAMMOSET database. The proposed method is likened with three state-of-the-art existing feature subset selection and classification approaches for validating the efficiency of the proposed approach. For Data set 1, the proposed method shows an accuracy of 90%, 92% and 98%, which is better than the existing methods taken for comparison. Also for Data set 2, the proposed method shows an accuracy of 97%, 91% and 94%, which is much better than the accuracy achieved by the existing approaches. Thus, the proposed approach outperforms the compared existing approaches by providing better precision, recall, F-measure, specificity and accuracy.

A Many-Objective Simultaneous Feature Selection and Discretization for LCS-Based Gesture Recognition

Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features; these two methods often lead to be more specific and concise data. In this paper, we propose to simultaneously deal with optimal feature subset selection, discretization, and classifier parameter tuning. As an illustration, the proposed problem formulation has been addressed using a constrained many-objective optimization algorithm based on dominance and decomposition (C-MOEA/DD) and a limited-memory implementation of the warping longest common subsequence algorithm (WarpingLCSS). In addition, the discretization sub-problem has been addressed using a variable-length representation, along with a variable-length crossover, to overcome the need of specifying the number of elements defining the discretization scheme in advance. We conduct experiments on a real-world benchmark dataset; compare two discretization criteria as discretization objective, namely Ameva and ur-CAIM; and analyze recognition performance and reduction capabilities. Our results show that our approach outperforms previous reported results by up to 11% and achieves an average feature reduction rate of 80%.

Epileptic Seizure Detection using Simulated Annealing based Optimal Feature Subset Selection with Kernel Extreme Learning Machine Classification Model

Epileptic Seizure Detection using Simulated Annealing based Optimal Feature Subset Selection with Kernel Extreme Learning Machine Classification Model

Analysis of various transfer functions for binary owl search algorithm in feature selection problem

ABSTRACT Owl Search Algorithm (OSA) is a recently proposed nature-inspired meta-heuristic algorithm which is easily implementable and exhibits great potential for solving continuous optimization problems. In our earlier work, a binary version of owl search algorithm (BOSA), with transfer functions for mapping the continuous solution space into a binary one, has been developed and applied in optimal feature subset selection problem. In our preliminary simulation experiments, it was found that the performance of the solution depends on the type of transfer function used. In this work, an extensive analysis of various types of transfer functions and their respective effects on the selection of optimal feature subset has been studied by simulation experiments with multiple benchmark datasets. Transfer functions of three different families, S-shaped, V-shaped and quadratic, are used for designing eleven BOSA models, each of which is made by combining individual transfer function. The performances of the proposed wrapper based feature subset selection algorithm based on several BOSA models have been evaluated by simulation experiments with twenty datasets for finding out the best model. The best observed BOSA model has also been compared with other similar meta-heuristics algorithms for feature subset selection. Experimental results show that the feature subset selected by BOSA with quadratic transfer function produces the highest classification accuracy with the minimum number of selected features compared to other algorithms.

Optimal Feature Subset Selection and MSVM Classification Based CBIR for Medical Images

Optimal Feature Subset Selection and MSVM Classification Based CBIR for Medical Images

Epileptic Seizure Detection using Simulated Annealing based Optimal Feature Subset Selection with Kernel Extreme Learning Machine Classification Model

Epileptic Seizure Detection using Simulated Annealing based Optimal Feature Subset Selection with Kernel Extreme Learning Machine Classification Model