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Cervical cancer is the most prevailing woman illness globally. Since cervical cancer is a very preventable illness, early diagnosis exhibits the most adaptive plan to lessen its global responsibility. However, because of infrequent knowledge, shortage of access to pharmaceutical centers, and costly schemes worldwide, most probably in emerging nations, the vulnerable subject populations cannot regularly experience the test. So, we need a clinical screening analysis to diagnose cervical cancer early and support the doctor to heal and evade cervical cancer?s spread in women?s other organs and save several lives. Thus, this paper introduces a novel hybrid approach to solve such problems: a hybrid feature selection approach with the Bayesian optimization-based optimized CatBoost (HFS-OCB) method to diagnose and predict cervical cancer risk. Genetic algorithm and mutual information approaches utilize feature selection methodology in this suggested research and form a hybrid feature selection (HFS) method to generate the most significant features from the input dataset. This paper also utilizes a novel Bayesian optimization-based hyperparameter optimization approach: optimized CatBoost (OCB) method to provide the most optimal hyperparameters for the CatBoost algorithm. The CatBoost algorithm is used to classify cervical cancer risk. There are two real-world, publicly available cervical cancer-based datasets utilized in this suggested research to evaluate and verify the suggested approach?s performance. A 20-fold cross-validation strategy and a renowned performance evaluation metric are utilized to assess the suggested approach?s performance. The outcome implies that the possibility of forming cervical cancer can be efficiently foretold using the suggested HFS-OCB method. Therefore, the suggested approach?s indicated result is compared with the other algorithms and provides the prediction. Such a predicted result shows that the suggested approach is more capable, reliable, scalable, and more effective than the other machine learning algorithms. [ABSTRACT FROM AUTHOR]

Accurately estimating the dolomite content in carbonate rocks is crucial for optimizing oil and gas exploration and production strategies. Hyperspectral techniques for estimating dolomite content have advantages in terms of efficiency, cost-effectiveness, and non-destructiveness compared with traditional laboratory methods. Despite the abundance of hyperspectral data, feature selection and extraction remain challenging. In this study, hyperspectral data collected from surface outcrop in the field using the analytical spectral device (ASD) were applied to construct model for estimating dolomite content. Firstly, the data were preprocessed via outlier analysis and continuum transformation. Next, a hybrid approach integrating spectral knowledge with machine learning was proposed and applied to facilitate efficient and precise feature selection of the hyperspectral data; in this approach, preliminary screening based on spectral knowledge is followed by further hyperspectral data feature selection using a random forest algorithm. The selected features were then combined using a support vector regression algorithm to obtain the estimation model. Finally, the accuracy of the model was evaluated using the hyperspectral data from field outcrop samples. To further verify the effectiveness of this method, various combinations of eight input variables and four machine learning algorithms were compared. Among all combinations, our model achieved the highest accuracy with a test R² value of 0.91 and a root-mean-square error of only 0.122. The proposed method is practical and efficient and provides precise quantitative data for field geologists to identify the mineral distribution in outcrops. Thus, our method provides robust support for understanding reservoir characteristics and has significant practical value in geological surveys and mineral exploration. [ABSTRACT FROM AUTHOR]

In the semiconductor manufacturing industry, achieving high yields constitutes one of the pivotal factors for sustaining market competitiveness. When confronting the substantial volume of high-dimensional, non-linear, and imbalanced data generated during semiconductor manufacturing processes, it becomes imperative to transcend traditional approaches and incorporate machine learning methodologies. By employing non-linear classification models, one can achieve more real-time anomaly detection, subsequently facilitating a deeper analysis of the fundamental causes behind anomalies. Given the considerable dimensionality of production line data in semiconductor manufacturing, there arises a necessity for dimensionality reduction to mitigate noise and reduce computational costs within the data. Feature selection stands out as one of the primary methodologies for achieving data dimensionality reduction. Utilizing wrapper-based heuristics algorithms, although characterized by high time complexity, often yields favorable performance in specific cases. If further combined into hybrid methodologies, they can concurrently satisfy data quality and computational cost considerations. Accordingly, this study proposes a two-stage feature selection model. Initially, redundant features are eliminated using mutual information to reduce the feature space. Subsequently, a Simplified Swarm Optimization algorithm is employed to design a unique fitness function aimed at selecting the optimal feature subset from candidate features. Finally, support vector machines are utilized as the classification model for validation purposes. For practical cases, it is evident that the feature selection method proposed in this study achieves superior classification accuracy with fewer features in the context of wafer anomaly classification problems. Furthermore, its performance on public datasets further substantiates the effectiveness and generalization capability of the proposed approach. [ABSTRACT FROM AUTHOR]

Drilling optimization has been broadly developed in terms of influential parameters. The assessment time and the effects of both geomechanical and drilling parameters were vital challenges of investigations. Drilling factors are applied force or rotation of drilling agents such as weight on bit (WOB), and geomechanical features represent mechanical indexes of rocks including unconfined compressive strength (UCS). Optimization efforts have been demonstrated on complex prediction methods whereas the simplicity of classification can offer some optimal ranges utilizing machine learning classifications in an accelerated process. In this study, a novel procedure using the supervised and semi-supervised learning approaches was conducted to classify and optimize the rate of penetration (ROP) and torque on bit (TOB). Firstly, in the case well, user-defined classes were assigned based on geomechanical units (GMU) and the ranges of high ROP and low TOB, thus classes divided drilling factors as GMUs of the case. Secondly, the feature selection was carried out by neural pattern recognition with three multi-objective optimization methods for classification. The inputs of classifications were WOB, hook load, pump pressure, flow rate, UCS, and internal friction angle. Classification approaches were decision trees, support vector machine (SVM), and ensemble learning. Finally, the bagged trees permutation and Laplacian SVM (LapSVM) algorithm separately revealed the significance of parameters and predicted the optimal ROP and TOB regions. Findings showed (1) in supervised classification of the case well, the cubic SVM and bagged trees had the highest area under the curve (AUC) and accuracy, on average 0.97 and 0.96, respectively. (2) The average accuracy of the supervised classifications in a test well was 91% except for the fine SVM, which makes them reliable for the fields with the least information. (3) The permutation outcomes for significant features, flow rate and UCS, exposed influential parameters for ROP and TOB optimization. (4) The semi-supervised method, LapSVM, not only acquired both ROP and TOB labels with an accuracy of 88% but also presented their optimal ranges in 95% of the assessed zones. (5) LapSVM deals with a limited training section perfectly opposed to the supervised version, which is vital for drilling investigation. (6) Implementing machine learning classification approaches with rock properties is a key factor in achieving effective drilling parameters in less time. More importantly, the recommended drilling factors concerning geomechanical properties can ameliorate both drilling performance and perception of upcoming collapse. [ABSTRACT FROM AUTHOR]

Abstract Drilling optimization has been broadly developed in terms of influential parameters. The assessment time and the effects of both geomechanical and drilling parameters were vital challenges of investigations. Drilling factors are applied force or rotation of drilling agents such as weight on bit (WOB), and geomechanical features represent mechanical indexes of rocks including unconfined compressive strength (UCS). Optimization efforts have been demonstrated on complex prediction methods whereas the simplicity of classification can offer some optimal ranges utilizing machine learning classifications in an accelerated process. In this study, a novel procedure using the supervised and semi-supervised learning approaches was conducted to classify and optimize the rate of penetration (ROP) and torque on bit (TOB). Firstly, in the case well, user-defined classes were assigned based on geomechanical units (GMU) and the ranges of high ROP and low TOB, thus classes divided drilling factors as GMUs of the case. Secondly, the feature selection was carried out by neural pattern recognition with three multi-objective optimization methods for classification. The inputs of classifications were WOB, hook load, pump pressure, flow rate, UCS, and internal friction angle. Classification approaches were decision trees, support vector machine (SVM), and ensemble learning. Finally, the bagged trees permutation and Laplacian SVM (LapSVM) algorithm separately revealed the significance of parameters and predicted the optimal ROP and TOB regions. Findings showed (1) in supervised classification of the case well, the cubic SVM and bagged trees had the highest area under the curve (AUC) and accuracy, on average 0.97 and 0.96, respectively. (2) The average accuracy of the supervised classifications in a test well was 91% except for the fine SVM, which makes them reliable for the fields with the least information. (3) The permutation outcomes for significant features, flow rate and UCS, exposed influential parameters for ROP and TOB optimization. (4) The semi-supervised method, LapSVM, not only acquired both ROP and TOB labels with an accuracy of 88% but also presented their optimal ranges in 95% of the assessed zones. (5) LapSVM deals with a limited training section perfectly opposed to the supervised version, which is vital for drilling investigation. (6) Implementing machine learning classification approaches with rock properties is a key factor in achieving effective drilling parameters in less time. More importantly, the recommended drilling factors concerning geomechanical properties can ameliorate both drilling performance and perception of upcoming collapse.

An Intrusion Detection System (IDS) is essential to identify cyber-attacks and implement appropriate measures for each risk. The efficiency of the Machine Learning (ML) techniques is compromised in the presence of irrelevant features and class imbalance. In this research, an efficient data pre-processing strategy was proposed to enhance the model’s generalizability. The class dissimilarity is addressed using k-Means SMOTE. After this, we furnish a hybrid feature selection method that combines filters and wrappers. Further, a hyperparameter-tuned Light Gradient Boosting Machine (LGBM) is analyzed by varying the optimal feature subsets. The experiments used the datasets – UNSW-NB15 and CICIDS-2017, yielding an accuracy of 90.71% and 99.98%, respectively. As the transparency and generalizability of the model depend significantly on understanding each component of the prediction, we employed the eXplainable Artificial Intelligence (XAI) method, SHapley Additive exPlanation (SHAP), to improve the comprehension of forecasted results.

Machine learning applications are increasingly challenged by the growing volume of data. In this context, selecting relevant features from the vast pool of extracted data remains a critical issue. This challenge is particularly prominent in facial expression recognition tasks, where the number of features extracted from facial images can be overwhelming. The quality of the selected features plays a pivotal role in determining the accuracy of facial expression recognition. To tackle this challenge effectively, we propose a novel three-phase hybrid feature selection method that leverages the strengths of filter, wrapper, and embedded algorithms. In the first phase, we employ correlation-based feature selection to reduce the search space, thereby enhancing computational efficiency and expediting the feature selection process. Subsequently, in the second phase, we apply recursive feature elimination to further refine feature selection. Finally, in the third phase, we utilize the XGBoost feature selection algorithm to identify the optimal subset of features. To evaluate the effectiveness of the proposed method, we have conducted experiments on five demanding facial expression recognition datasets: DISFA, CK+, DISFA+, JAFFE, and FER2013, while using key metrics, including accuracy, precision, recall, and F1-Score. The obtained results demonstrate the efficiency of our method, achieving an accuracy of 94.68%, 98.82%, 95.8%, 97.7%, and 66.1% on DISFA, CK+, DISFA+, JAFFE, and FER2013 datasets, respectively. [ABSTRACT FROM AUTHOR]

Integrating the feature selection process into machine learning-based transient analysis (MLTA) is essential for ensuring precise and timely transient stability prediction (TSP). Therefore, it’s important to prioritize the development of a hybrid feature selection scheme (FSS) to find optimal transient features (OTFs) from high-dimensional transient data within MLTA projects. This study introduces the double filter-encircled wrappers FSS (DFEWFSS) to survive OTFs from transient 28-trajectory data. Each fold of DFEWFSS consists of interconnected encircled wrappers by filters. The filter phase is exerted by relevancy score (RS) and conditional RS (CRS) which stem from mutual information (MI) and conditional MI (CMI). The wrapper phase is conducted by kernelized hyperplane-based classifiers, which are mounted on incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr). Following the completion of OTFs, the evaluation of OTFs in TSP is carried out through a cross-validation scenario. The results show that DFEWFSS-based OTFs have a prediction accuracy of 99 % and a processing time of 102.155 milliseconds for TSP.

Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data–based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages — stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 — minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 — mRMR, mutual information (MI) and CFS, and Pipeline 3 — mRMR and support vector machine–recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further. [ABSTRACT FROM AUTHOR]

The option to steal a significant quantity of important information without the owner of the message's consent is provided by keyloggers, which are tools designed to record each keystroke made on the computer. Online criminals often employ malware-infected software to attack mobile devices like smartphones and tablets. In addition, hackers are becoming smarter over time. It will be easier for them to add a keylogger to a website than a software program because users must download and install it on their devices before accessing it. All these processes, however, are not required for a website. Websites can be run on any platform. They might pick any user as their target. Thus, social networking sites, internet banking, and emails are accessible to hackers. This paper aims to develop a machine learning-based model for an in-website keylogger detection for platform-independent devices to enhance internet users' privacy and security. The study employs Random Forest, LightGBM, and CatBoost as classifiers, and uses a hybrid feature selection method, known as Hybrid Ensemble Feature Selection (HEFS), which makes the identification process robust and less runtime complex. When comparing the selected and full features on the adopted classifiers, Random Forest was found to be the best in performance; it experienced a minimal accuracy deterioration of 1.59% while achieving a massive 84.5% reduction in feature space. [ABSTRACT FROM AUTHOR]

Feature selection is a promising Artificial Intelligence technique for screening, analysing, predicting, and tracking current COVID-19 patients and likely future patients. Significant applications are developed to track data of confirmed, recovered, and death cases. In this work, we propose a new feature selection method based on a new way of hybridization between filter and wrapper methods. The proposed approach is expected to achieve high classification accuracy with a small feature subset. Specifically, the main contribution of this work is a four steps-based approach organized as follows: First, we remove consecutively duplicate and constant features. Then, we select the highest-ranked feature with Mutual Information. In the last step, we run the 'Backward Feature Elimination' algorithm to delete features from the active subset until a stopping criterion based on the degradation of classification performance is met. We applied the proposed approach to a COVID-19 dataset to test its ability to find the relevant feature for characterizing the disease, such as new cases infected with the virus, people vaccinated, and the number of deaths, to better assess the situation. For evaluation purposes, experiments are conducted at the first stage on the COVID-19 dataset, then on six benchmark datasets that have a high dimensional and large size. The method performance is tracked and measured on these datasets and a comparison with many approaches is provided. [ABSTRACT FROM AUTHOR]

Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated text content available in social media forums offers an opportunity to build automatic and reliable depression detection models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM) classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result, the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the depression detection system.

Background: In the current study, a hybrid feature selection approach involving filter and wrapper methods is applied to some bioscience databases with various records, attributes and classes; hence, this strategy enjoys the advantages of both methods such as fast execution, generality, and accuracy. The purpose is diagnosing of the disease status and estimating of the patient survival. Methods: Feature selection algorithms have been modeled in Matlab R2021a during April and May 2022 in the framework of statistical pattern recognition. First, the features are ranked based on normalized mutual information, as a metric of relevance and redundancy of features, and accordingly, an optimum feature subset with the highest accuracy of classification is selected. Two feature selection algorithms, i.e., inclusion of features enhancing the classification accuracy and exclusion of irrelevant features are applied to the interest datasets, subsequent to the mini-batch k-means clustering of records. Results: At the end of the execution of both feature selection methods, evaluation metrics including accuracy, precision, recall, and F1 score are measured and compared. Both proposed feature selection approaches for the molecular biology, hepatitis C virus (HCV), and E. coli bacteria datasets result in the precision and recall scores more than 98 percent, meaning that there are few false positives and false negatives in the linear support vector machine (LSVM) classification. Regarding the HCV dataset, selection of nine relevant features among the thirteen present ones using the feature exclusion method yields the classification accuracy and F1 score of 98.92 percent and 99.02 percent, respectively. The feature inclusion approach also results in an accuracy of 98.78 percent with a slight discrepancy. Conclusion: The results reveal superior strength of the feature selection methods used here for life science datasets with higher-order features such as protein/gene expression database. The potentials to generalize to other classifiers and automatically specify the optimal number of features during the feature selection procedure make these approaches flexible in many data mining applications for the life sciences.

Many women around the world die due to breast cancer. If breast cancer is treated in the early phase, mortality rates may significantly be reduced. Quite a number of approaches have been proposed to help in the early detection of breast cancer. A novel hybrid feature selection model is suggested in this study. This novel hybrid model aims to build an efficient feature selection method and successfully classify breast lesions. A combination of relief and binary Harris hawk optimization (BHHO) hybrid model is used for feature selection. Then, k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR) and naive Bayes (NB) methods are preferred for the classification task. The suggested hybrid model is tested by three different breast cancer datasets which are Wisconsin diagnostic breast cancer dataset (WDBC), Wisconsin breast cancer dataset (WBCD) and mammographic breast cancer dataset (MBCD). According to the experimental results, the relief and BHHO hybrid model improves the performance of all classification algorithms in all three datasets. For WDBC, relief-BHO-SVM model shows the highest classification rates with an of accuracy of 98.77%, precision of 97.17%, recall of 99.52%, F1-score of 98.33%, specificity of 99.72% and balanced accuracy of 99.62%. For WBCD, relief-BHO-SVM model achieves of accuracy of 99.28%, precision of 98.76%, recall of 99.17%, F1-score of 98.96%, specificity of 99.56% and balanced accuracy of 99.36%. Relief-BHO-SVM model performs the best with an accuracy of 97.44%, precision of 97.41%, recall of 98.26%, F1-score of 97.84%, specificity of 97.47% and balanced accuracy of 97.86% for MBCD. Furthermore, the relief-BHO-SVM model has achieved better results than other known approaches. Compared with recent studies on breast cancer classification, the suggested hybrid method has achieved quite good results. [ABSTRACT FROM AUTHOR]

Research in AI has proved to be revolutionarily beneficial to humankind from the past few decades. Many supporting techniques have been developed that indirectly evolved AI and directly enhanced various machine learning models; one of them being the feature engineering. It can also be considered as applied-ML. Another is the so-called feature selection which is method in which most contributing feature to final decision making, out of the entire feature space are selected for processing into an ML model. It is not an easy task to precisely calculate the dependency of the output variable onto the candidate features, particularly when the data is high in dimensions. In this regard, this study proposes a novel method named the cisoidal analysis based feature selection (CAFS) which uses both pre-established algorithms as well as a new approach of relating members of feature space to a complex sinusoid (cisoid) mathematically, then using signal processing techniques to eliminate certain elements in the entire feature space for enhanced feature selection and hence to obtain higher classification accuracy. Derived from experiments with five high dimensional datasets, CAFS displays significantly competitive performance than some of the pre-existing algorithms. CAFS is highly advantageous in reducing dimension of feature space in most of the applications. [ABSTRACT FROM AUTHOR]

Death from heart disease has consistently ranked high among the leading causes of mortality in the globe. Predicting the likelihood of getting heart disease based on certain features is crucial because of the high cost of identifying heart disease. Feature extraction is an important step in any classification system as it reduces dimensionality and enhances accuracy. This paper proposes a technique called Data Normalization and Feature Selection Using Hybrid Methods (DNFH) for predicting the likelihood of getting heart disease based on selected features. This technique uses Weighted Transform KMeans Clustering (WTKMC) for data normalization; Elastic Net (EN), Random Forest Classifier (RFC), Binary BAT (BBA), and Weighted Binary BAT Algorithm (WBBAT) for ensemble feature selection. Experiments were conducted using the Cleveland dataset and classification accuracy was compared with Support Vector Machine (SVM) and Logistic Regression (LR) models. The results showed significant improvement in accuracy using WTKMC for preprocessing and ensemble feature selection techniques. [ABSTRACT FROM AUTHOR]

Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated text content available in social media forums offers an opportunity to build automatic and reliable depression detection models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM) classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result, the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the depression detection system. [ABSTRACT FROM AUTHOR]

One of the major problems in microarray datasets is the large number of features, which causes the issue of "the curse of dimensionality" when machine learning is applied to these datasets. Feature selection refers to the process of finding optimal feature set by removing irrelevant and redundant features. It has a significant role in pattern recognition, classification, and machine learning. In this study, a new and efficient hybrid feature selection method, called Garank&rand, is presented. The method combines a wrapper feature selection algorithm based on the genetic algorithm (GA) with a proposed filter feature selection method, SLI-γ. In Garank&rand, some initial solutions are built regarding the most relevant features based on SLI-γ, and the remaining ones are only the random features. Eleven high-dimensional and standard datasets were used for the accuracy evaluation of the proposed SLI-γ. Additionally, four high-dimensional well-known datasets of microarray experiments were used to carry out an extensive experimental study for the performance evaluation of Garank&rand. This experimental analysis showed the robustness of the method as well as its ability to obtain highly accurate solutions at the earlier stages of the GA evolutionary process. Finally, the performance of Garank&rand was also compared to the results of GA to highlight its competitiveness and its ability to successfully reduce the original feature set size and execution time. [ABSTRACT FROM AUTHOR]

Objective. Myocardial infarction (MI) is one of the leading causes of human mortality in all cardiovascular diseases globally. Currently, the 12-lead electrocardiogram (ECG) is widely used as a first-line diagnostic tool for MI. However, visual inspection of pathological ECG variations induced by MI remains a great challenge for cardiologists, since pathological changes are usually complex and slight. Approach. To have an accuracy of the MI detection, the prominent features extracted from in-depth mining of ECG signals need to be explored. In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set. Main results. Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%. Significance. The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients. [ABSTRACT FROM AUTHOR]

With the advent of big data era and the rapid improvement of raw data scale, feature selection, as the basis and critical technologies for data mining, plays an increasingly important role. However, most studies on feature selection methods, mainly directed to treat the single feature or overall feature subset, while the influence of the correlation and redundancy of features in the feature subset on the classification results is ignored. In this paper, a hybrid feature selection method based on feature subsets generated by factor analysis (FAFS_HFS) is proposed. Firstly, this method generates feature subsets from the maximum load (maximum explanatory power) of each feature through factor analysis. Then, minimal redundancy and maximal relevance (mRMR) and sequential forward selection (SFS) are used to remove the redundancy of each feature subset. Finally, fisher score based on feature subset (FSF-score) is utilized to evaluate and obtain the optimal feature subsets. Experiments are conducted on 14 datasets, the results show that FAFS_HFS method has higher classification accuracy and lower dimension on almost all datasets, especially in high-dimensional datasets, and it has competitive efficiency and classification performance compared with other methods.

The rate of increase in skin cancer incidences has become worrying in recent decades. This is because of constraints like eventual draining of ozone levels, air's defensive channel capacity and progressive arrival of Sun-oriented UV radiation to the Earth's surface. The failure to diagnose skin cancer early is one of the leading causes of death from the disease. Manual detection processes consume more time well as not accurate, so the researchers focus on developing an automated disease classification method. In this paper, an automated skin cancer classification is achieved using an adaptive neuro-fuzzy inference system (ANFIS). A hybrid feature selection technique was developed to choose relevant feature subspace from the dermatology dataset. ANFIS analyses the dataset to give an effective outcome. ANFIS acts as both fuzzy and neural network operations. The input is converted into a fuzzy value using the Gaussian membership function. The optimal set of variables for the Membership Function (MF) is generated with the help of the firefly optimization algorithm (FA). FA is a new and strong meta-heuristic algorithm for solving nonlinear problems. The proposed method is designed and validated in the Python tool. The proposed method gives 99% accuracy and a 0.1% false-positive rate. In addition, the proposed method outcome is compared to other existing methods like improved fuzzy model (IFM), fuzzy model (FM), random forest (RF), and Naive Byes (NB).

Maize stalk rot infection is one of the most important causes of production loss in some regions of India. However, it is difficult to diagnose maize disease on a wide scale due to a lack of human competency and limitations in standard image-recognition technologies. Fortunately, improvements in deep learning and machine learning technologies enable automatic disease identification. In this study, we proposed a 4-stage cascaded approach for the classification of Fusarium stalk rot and Charcoal rot in maize. In the proposed model, deep features are extracted by using three different convolutional neural network (CNN) models namely: GoogLeNet, ResNet18 and MobileNetV2 from the maize images which are preprocessed in the previous stage. The features extracted from these three CNN models are concatenated to benefit each CNN structure, and then the hybrid feature selection approach is utilized to choose the most relevant features with the lowest dimension to train the support vector machine (SVM) classifier. The findings of the experiment reveal that the proposed cascade model can classify the diseased as well as healthy images with an accuracy of 97.95%. Furthermore, the suggested model performed better in terms of specificity, accuracy, and F1-Score, with values of 0.98, 0.95, and 0.96, respectively. This study also presents a comparison of the performance of the proposed cascade model to that of several other relevant classification models.

Developing a deformation prediction model with strong robustness and applicability based on monitoring data is an important task in establishing a safety monitoring system for super high arch dams. This study proposes a hybrid deep learning (DL) model for deformation prediction, named HFS/xs-MLSTM. The proposed multidimensional long short-term memory (MLSTM) DL achieves integrated modeling of two factors, environment and temporal correlation, by constructing a multivariate matrix, and achieves sequential prediction by rolling through the sliding windows. This operation solves the problem of insufficient prediction accuracy due to the traditional method considering only a single factor. To address the problems of computational time consumption and DL overfitting caused by variable redundancy, a hybrid feature selection method (HFS/xs) based on eXtreme gradient boosting and Spearman is proposed. Analysis shows that HFS/xs can comprehensively reflect the intrinsic laws of the data and effectively filter out the most concise set of variables, which greatly improves the operability and stability of MLSTM. Compared with traditional methods, HFS/xs-MLSTM has the best performance and applicability for deformation prediction in different zones and operating conditions. This study can provide reliable a priori knowledge for the construction of safety monitoring system for super high arch dams. [ABSTRACT FROM AUTHOR]

Background: In the current study, a hybrid feature selection approach involving filter and wrapper methods is applied to some bioscience databases with various records, attributes and classes; hence, this strategy enjoys the advantages of both methods such as fast execution, generality, and accuracy. The purpose is diagnosing of the disease status and estimating of the patient survival. Methods: Feature selection algorithms have been modeled in Matlab R2021a during April and May 2022 in the framework of statistical pattern recognition. First, the features are ranked based on normalized mutual information, as a metric of relevance and redundancy of features, and accordingly, an optimum feature subset with the highest accuracy of classification is selected. Two feature selection algorithms, i.e., inclusion of features enhancing the classification accuracy and exclusion of irrelevant features are applied to the interest datasets, subsequent to the mini-batch k-means clustering of records. Results: At the end of the execution of both feature selection methods, evaluation metrics including accuracy, precision, recall, and F1 score are measured and compared. Both proposed feature selection approaches for the molecular biology, hepatitis C virus (HCV), and E. coli bacteria datasets result in the precision and recall scores more than 98 percent, meaning that there are few false positives and false negatives in the linear support vector machine (LSVM) classification. Regarding the HCV dataset, selection of nine relevant features among the thirteen present ones using the feature exclusion method yields the classification accuracy and F1 score of 98.92 percent and 99.02 percent, respectively. The feature inclusion approach also results in an accuracy of 98.78 percent with a slight discrepancy. Conclusion: The results reveal superior strength of the feature selection methods used here for life science datasets with higher-order features such as protein/gene expression database. The potentials to generalize to other classifiers and automatically specify the optimal number of features during the feature selection procedure make these approaches flexible in many data mining applications for the life sciences. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) remains the most dominant cancer among women worldwide. Numerous BC gene expression microarray-based studies have been employed in cancer classification and prognosis. The availability of gene expression microarray data together with advanced classification methods has enabled accurate and precise classification. Nevertheless, the microarray datasets suffer from a large number of gene expression levels, limited sample size, and irrelevant features. Additionally, datasets are often asymmetrical, where the number of samples from different classes is not balanced. These limitations make it difficult to determine the actual features that contribute to the existence of cancer classification in the gene expression profiles. Various accurate feature selection methods exist, and they are being widely applied. The objective of feature selection is to search for a relevant, discriminant feature subset from the basic feature space. In this review, we aim to compile and review the latest hybrid feature selection methods based on bio-inspired metaheuristic methods and wrapper methods for the classification of BC and other types of cancer. [ABSTRACT FROM AUTHOR]

This paper presents the application of heterogeneous transfer learning (HetTL) methods which consider hybrid feature selection to reduce the training calibration effort and the noise generated by fingerprint duplicates obtained from multiple Wi-Fi access points. The Cramer–Rao Lower Bound analysis (CRLB) was also applied to evaluate and estimate a lower limit for the variance of a parameter estimator used to analyze positioning performance. We developed two novel algorithms for feature selection in fingerprint-based indoor positioning problems (IPP) to enhance positioning performance in the target domain with the HetTL. The algorithms comprised two scenarios: (i) a principal component analysis-based approach (PCA-based) and (ii) a hybrid approach that takes both PCA and correlation effect analysis into account (hybrid scenario). Accordingly, a new feature vector was constructed by retaining only the most significant predictors, and the most efficient feature dimensions were also determined by using a hybrid-based approach. Experimental results showed that the hybrid-based proposed algorithm has the minimum mean absolute error. The CRLB analysis also showed that the number of Wi-Fi access points could affect the lower bound location estimation error; however, identifying the most significant predictors is an effective approach to improve positioning performance. [ABSTRACT FROM AUTHOR]

Cancer is one of the second deadliest diseases in the world after heart disease. Citing from the WHO's report on cancer, in 2018 there were around 18.1 million cases of cancer in the world with a total of 9.6 million deaths. Now that bioinformatics technology is growing and based on WHO’s report on cancer, an early detection is needed where bioinformatics technology can be used to diagnose cancer and to help to reduce the number of deaths from cancer by immediately treating the person. Microarray DNA data as one of the bioinformatics technology is becoming popular for use in the analysis and diagnosis of cancer in the medical world. Microarray DNA data has a very large number of genes, so a dimensional reduction method is needed to reduce the use of features for the classification process by selecting the most influential features. After the most influential features are selected, these features are going to be used for the classification and predict whether a person has cancer or not. In this research, hybridization is carried out by combining Information Gain as a filtering method and Genetic Algorithm as a wrapping method to reduce dimensions, and lastly FLNN as a classification method. The test results get colon cancer data to get the highest accuracy value of 90.26%, breast cancer by 85.63%, lung cancer and ovarian cancer by 100%, and prostate cancer by 94.10%.

Sentiment analysis has recently drawn considerable research attentions in the recent years owing to its applicability in determining users opinion, sentiment and emotions from large collections of textual data. The goal of sentiment analysis centered on improving users experience by deploying robust techniques that mine opinions and emotions from large corpora. Although there are a number of studies on sentiment analysis and opinion mining from textual information, however, the existence of domain-specific words such as slang, abbreviations and grammatical mistakes further posed serious challenges to existing sentiment analysis methods. Therefore, research efforts have focused on finding the most discriminative attributes that can help in capturing users opinions from textual datasets. In this paper, we focused on identification of effective discriminative subset of features that can aid classification of users opinion from large corpora. This study proposed hybrid feature selection framework that is based on hybridization of filter- and wrapper-based feature selection methods. Correlation feature selection (CFS), a filter-based approach is hybridized with Boruta and Recursive Feature Elimination (RFE), which are wrapper-based feature selection methods, to identify the most discriminative features subsets for sentiment analysis. Four publicly available datasets for sentiment analysis: Amazon, Yelp, IMDB and Kaggle were considered to evaluate the performance of the proposed hybrid feature selection framework. This study evaluated the performance of three classification algorithms: Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) to ascertain the superiority of the proposed approach. Experimental results across different contexts as depicted by the datasets considered in this study clearly showed that CFS combined with Boruta produced promising results especially when the features selected are passed to RF classifier. Indeed, the proposed hybrid framework provide effective way of predicting users opinions and emotions while giving substantial consideration to predictive accuracy [JJCIT 2021; 7(2.000): 130-151]

Stock market volatility has a significant impact on many economic and financial activities in the world. Forecasting stock price movement plays an important role in setting an investment strategy or determining the right timing for trading. However, stock price movements are noisy, nonlinear, and chaotic. It is difficult to forecast stock trends for improving return on investment. Here, we proposed a novel improved particle swarm optimization (IPSO) and long-short term memory (LSTM) hybrid model for stock price forecasting. An adaptive mutation factor was used as a parameter for model optimization to avoid premature convergence to a local optimum. Furthermore, we presented a nonlinear approach to improve the inertia weight of the particle swarm optimization and then used the IPSO model to optimize the hyperparameters of LSTM. The experimental results showed that the proposed model outperformed the other related baseline models: support-vector regression, LSTM and PSO-LSTM on the Australian stock market index. These results indicated the proposed model possesses high reliability and good forecasting capability.

The high dimension, high redundancy and class imbalance of cancer multiple omics data are the main challenges for cancer diagnosis. Existing studies have neglected the role of functional proteomics in the occurrence and development of cancer. In this study, a novel hybrid feature selection and ensemble learning framework, referred to as the three-stage feature selection and twice-competitional ensemble learning method (TSFS-TCEM), is proposed for cancer diagnosis. Firstly, we combine the transcriptome and functional proteomics data to construct a multi-omics data on breast cancer, which is the first time to apply these combined biological data for diagnosing breast cancer. Secondly, the proposed method introduces multiple models during the feature selection and diagnostic model construction. The three-stage feature selections integrate the features from different types of data and the twice-competitional ensemble learning framework resolves the data imbalance problem suffer from a single classifier. The TSFS-TCEM achieves a diagnostic accuracy of 99.64%, outperforming all compared methods. In addition, the 5-fold cross-validation sensitivity, specificity and F-Measure of the method are above 99.63%.

In recent years, sensor-based human activity recognition (HAR) has become a hot topic due to the advancement of sensing technologies, wireless communication technologies and nano-technologies. Since the sensor signals are usually non-stationary and quite noisy, both selecting the discriminant feature representations and finding out the optimal parameters for recognition algorithm play an important role for the enhanced performance and robustness of an HAR system. However, most of the previous research focused on one of them ignoring their interactions. Very few studies focused on these two aspects simultaneously. Considering the two factors separately may lead to inferior HAR performance. This paper presents a novel HAR framework which can optimize the feature set and the parameters of recognition algorithm synchronously for robust and optimal system performance. A new hybrid feature selection methodology using game-theory based feature selection (GTFS) and binary firefly algorithm (BFA), called GTFS-BFA, is proposed. GTFS-BFA is a hybrid methodology combining evidence from both filter and wrapper feature selection methods. It consists of two phases, namely pre-selection phase and re-selection phase. Pre-selection phase relies on game-theory-based filter method, while the re-selection phase uses binary firefly algorithm (BFA) as a wrapper method. The popular and efficient algorithm kernel extreme learning machine (KELM) is utilized as a classifier. The experimental results indicate that the proposed method can obtain better comprehensive performance in terms of four performance measures through a comparison to other existing methods on daily activity dataset from five body positions.

Sentiment Analysis (SA) aims to extract useful information from online Unstructured User-Generated Contents (UUGC) and classify them into positive and negative classes. State-of-the-art techniques for SA suffer a high dimensional feature space because of noisy and irrelevant features from the UUGC. Researchers have also proposed feature extraction and selection techniques to reduce high dimensional feature space, but they fall short in extracting and selecting the most effective sentiment features for sentiment model learning. Effective feature extraction and selection are significant for the SA because they can boost the learning algorithm’s predictive performance while reducing the high-dimensional feature space. To address these concerns, we propose an Intelligent Hybrid Feature Selection for Sentiment Analysis (IHFSSA) based on ensemble learning methods. IHFSSA first identifies sentiment features in the review text utilizing Penn Treebank part-of-speech tagset and integrated Wide Coverage Sentiment Lexicons (WCSL). The sentiment features subset is then selected employing a fast and simple rank-based ensemble of multiple filters feature selection method. The selected sentiment features are further refined by applying a wrapper-based backward feature selection method. Finally, for textual sentiment classification, the well-known classification algorithms Support Vector Machine (SVM), Naive Bayes (NB), Generalized Linear Model (GLM) are trained in the ensemble model on the refined sentiment feature set. The in-depth evaluation using heterogeneous domain benchmark datasets demonstrates that IHFSSA outperforms existing SA techniques.

Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions, for the improvement of quality of education and to meet the dynamic needs of society. The selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy, but also helps in building the strategic plans for the improvement of students’ academic performance. There are different feature selection algorithms for predicting the performance of students, however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal features. In this paper, a hybrid feature selection framework (using feature-fusion) is designed to identify the significant features and associated features with target class, to predict the performance of students. The main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy, but also to identify optimal features for building productive strategies for the improvement in students’ academic performance. The key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework, is two level feature fusion technique, with the utilization of cosine-based fusion. Whereas, according to the results reported in existing literature, cosine similarity is considered as the best similarity measure among existing similarity measures. The proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of instances. The validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework, existing feature selection algorithms in terms of accuracy, f-measure, recall, and precision. Results reported in presented paper show that the proposed approach gives more than 90% accuracy on benchmark dataset that is better than the results of existing approach. [ABSTRACT FROM AUTHOR]

The rate of increase in skin cancer incidences has become worrying in recent decades. This is because of constraints like eventual draining of ozone levels, air's defensive channel capacity and progressive arrival of Sun-oriented UV radiation to the Earth's surface. The failure to diagnose skin cancer early is one of the leading causes of death from the disease. Manual detection processes consume more time well as not accurate, so the researchers focus on developing an automated disease classification method. In this paper, an automated skin cancer classification is achieved using an adaptive neuro-fuzzy inference system (ANFIS). A hybrid feature selection technique was developed to choose relevant feature subspace from the dermatology dataset. ANFIS analyses the dataset to give an effective outcome. ANFIS acts as both fuzzy and neural network operations. The input is converted into a fuzzy value using the Gaussian membership function. The optimal set of variables for the Membership Function (MF) is generated with the help of the firefly optimization algorithm (FA). FA is a new and strong meta-heuristic algorithm for solving nonlinear problems. The proposed method is designed and validated in the Python tool. The proposed method gives 99% accuracy and a 0.1% false-positive rate. In addition, the proposed method outcome is compared to other existing methods like improved fuzzy model (IFM), fuzzy model (FM), random forest (RF), and Naive Byes (NB). [ABSTRACT FROM AUTHOR]