Swarm Optimization Based Feature Selection Research Articles

Particle Swarm Optimization Based Feature Selection Using Factorial Design

Feature selection, a common and crucial problem in current scientific research, is a crucial data preprocessing technique and a combinatorial optimization task. Feature selection aims to select a subset of informative and appropriate features from the original feature dataset. Therefore, improving performance on the classification task requires processing the original data using a feature selection strategy before the learning process. Particle swarm optimization, one of the metaheuristic algorithms that prevents the growth of computing complexity, can solve the feature selection problem satisfactorily and quickly with appropriate classification accuracy since it has local optimum escape strategies. There are arbitrary trial and error approaches described separately in the literature to determine the critical binary particle swarm optimization parameters, which are the inertial weight, the transfer function, the threshold value, and the swarm size, that directly affect the performance of the binary particle swarm optimization algorithm parameters used in feature selection. Unlike these approaches, this paper enables us to obtain scientific findings by evaluating all binary particle swarm optimization parameters together with the help of a statistically based factorial design approach. The results show how well the threshold and the transfer function have statistically affected the binary particle swarm optimization algorithm performance.

Oppositional Cat Swarm Optimization-Based Feature Selection Approach for Credit Card Fraud Detection.

Credit card fraud has drastically increased in recent times due to the advancements in e-commerce systems and communication technology. Falsified credit card transactions affect the financial status of the companies as well as clients regularly and fraudsters incessantly try to develop new approaches to commit frauds. The recognition of credit card fraud is essential to sustain the trustworthiness of e-payments. Therefore, it is highly needed to design effective and accurate credit card fraud detection (CCFD) techniques. The recently developed machine learning (ML) and deep learning (DL) can be employed for CCFD because of the characteristics of building an effective model to identify fraudulent transactions. In this view, this study presents a novel oppositional cat swarm optimization-based feature selection model with a deep learning model for CCFD, called the OCSODL-CCFD technique. The major intention of the OCSODL-CCFD technique is to detect and classify fraudulent transactions using credit cards. The OCSODL-CCFD technique derives a new OCSO-based feature selection algorithm to choose an optimal subset of features. Besides, the chaotic krill herd algorithm (CKHA) with the bidirectional gated recurrent unit (BiGRU) model is applied for the classification of credit card frauds, in which the hyperparameter tuning of the BiGRU model is performed using the CKHA. To demonstrate the supreme outcomes of the OCSODL-CCFD model, a wide range of simulation analyses were carried out. The extensive comparative analysis highlighted the better outcomes of the OCSODL-CCFD model over the compared ones based on several evaluation metrics.

Artificial Intelligence Based Customer Churn Prediction Model for Business Markets.

The introduction of artificial intelligence (AI) and machine learning (ML) technologies in recent years has resulted in improved company performance. Customer churn forecast is a difficult problem in many corporate sectors, particularly the telecommunications industry. Because customer churns have a direct impact on a company's total revenue, telecommunications firms have begun to develop 76 models to reduce churns at an earlier stage. Previous research has revealed that AI and ML models are effective CCP solutions. According to this viewpoint, this study proposes a unique AI-based CCP model for Telecommunication Business Markets (AICCP-TBM). The AICCP-TBM model's purpose is to control the existence of churners and non-churners in the telecom sector. The proposed AICCP-TBM model employs a Chaotic Salp Swarm Optimization-based Feature Selection (CSSO-FS) method for the best feature assortment. In addition, a Fuzzy Rule-based Classifier(FRC) is used to distinguish between client churners and non-churners. A technique known as Quantum Behaved Particle Swarm Optimization (QPSO) is used to pick the membership functions for the FRC model in order to improve the classification performance of the FRC model. The performance of the AICCP-TBM model is validated using a benchmark CCP dataset and the experimental results are reviewed from several angles. In relations of presentation, the imitation consequences demonstrated that the AICCP-TBM model surpassed the most recent state-of-the-art CPP models. The suggested AICCP-TBM method's comparative accuracy was thoroughly tested on the three datasets used. Using datasets 1-3, this technique obtained better levels of accuracy, with the maximum attainable values being 97.25 %, 97.5 % and 94.33 %. The simulation results for the AICCP-TBM model demonstrated improved prediction performance.

Chicken Swarm-Based Feature Subset Selection with Optimal Machine Learning Enabled Data Mining Approach

Data mining (DM) involves the process of identifying patterns, correlation, and anomalies existing in massive datasets. The applicability of DM includes several areas such as education, healthcare, business, and finance. Educational Data Mining (EDM) is an interdisciplinary domain which focuses on the applicability of DM, machine learning (ML), and statistical approaches for pattern recognition in massive quantities of educational data. This type of data suffers from the curse of dimensionality problems. Thus, feature selection (FS) approaches become essential. This study designs a Feature Subset Selection with an optimal machine learning model for Educational Data Mining (FSSML-EDM). The proposed method involves three major processes. At the initial stage, the presented FSSML-EDM model uses the Chicken Swarm Optimization-based Feature Selection (CSO-FS) technique for electing feature subsets. Next, an extreme learning machine (ELM) classifier is employed for the classification of educational data. Finally, the Artificial Hummingbird (AHB) algorithm is utilized for adjusting the parameters involved in the ELM model. The performance study revealed that FSSML-EDM model achieves better results compared with other models under several dimensions.

Binary Particle Swarm Optimization-Based Feature Selection for Predicting the Class of the Knee Angle from EMG Signals in Lower Limb Movements

Binary Particle Swarm Optimization-Based Feature Selection for Predicting the Class of the Knee Angle from EMG Signals in Lower Limb Movements

Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment.

In recent days, Cognitive Cyber-Physical System (CCPS) has gained significant interest among interdisciplinary researchers which integrates machine learning (ML) and artificial intelligence (AI) techniques. This era is witnessing a rapid transformation in digital technology and AI where brain-inspired computing-based solutions will play a vital role in industrial informatics. The application of CCPS with brain-inspired computing in Industry 4.0 will create a significant impact on industrial evolution. Though the CCPSs in industrial environment offer several merits, security remains a challenging design issue. The rise of artificial intelligence AI techniques helps to address cybersecurity issues related to CCPS in industry 4.0 environment. With this motivation, this paper presents a new AI-enabled multimodal fusion-based intrusion detection system (AIMMF-IDS) for CCPS in industry 4.0 environment. The proposed model initially performs the data pre-processing technique in two ways namely data conversion and data normalization. In addition, improved fish swarm optimization based feature selection (IFSO-FS) technique is used for the appropriate selection of features. The IFSO technique is derived by the use of Levy Flight (LF) concept into the searching mechanism of the conventional FSO algorithm to avoid the local optima problem. Since the single modality is not adequate to accomplish enhanced detection performance, in this paper, a weighted voting based ensemble model is employed for the multimodal fusion process using recurrent neural network (RNN), bi-directional long short term memory (Bi-LSTM), and deep belief network (DBN), depicts the novelty of the work. The simulation analysis of the presented model highlighted the improved performance over the recent state of art techniques interms of different measures.

Particle Swarm Optimization Based Feature Selection for Educational Data Mining

Particle Swarm Optimization Based Feature Selection for Educational Data Mining

An Optimal Binary Particle Swarm Optimization Based Feature Selection Model for Big Data Analysis of Product Assessment

Big data defines the state where the size, speed and kind of data go beyond a memory or executing capabilities for precise and timely decision-making. Big data analytics is integrated with ML and statistical methods for processing big data and recognizes the important data. At present times, the generation of online product reviews has exponentially increased at each and every second. These applications have resulted in developing the volumes of data which can be used for prediction and classification for decision making process. Compared with other models, various techniques are applied in solving the big data problem, feature selection (FS) is known to be an efficient method. FS operations could be exploring with the application of a subset of features which is related to the topic of précised definition of the existing datasets. Deplorably, search using this type of sub-sets results in the problems of combinatorial as well as maximum time consuming. The meta-heuristic approaches are typically employed to facilitate the choice of features. This paper presents an optimal extreme learning machine (ELM) based binary particle swarm optimization to precede the FS process. The proposed method develops a Fitness Function (FF) by applying ELM. And the best solution of the FF has been explored under the application of BPSO technique. For instance, the dataset of product review which are derived from Amazon including synthetic data, which is comprised with total of 235,000 positive and 147,000 negative review records is used. The experimental result implied that the ELM-BPSO technique is comparably best

Complement component face space for 3D face recognition from range images

This paper proposes a mathematical model for decomposing a range face image into four basic components (named ‘complement components’) in conjunction with a simple approach for data-level fusion to generate thirty-six additional hybrid components. These forty component faces composing a new face image space called the ‘complement component face space.’ The main challenge of this work was to extract relevant features from the vast face space. Features are extracted from the four basic components and four selected hybrid components using singular value decomposition. To introduce diversity, the extracted feature vectors are fused by applying the crossover operation of the genetic algorithm using a Hamming distance-based fitness measure. Particle swarm optimization-based feature selection is employed on the fused features to discard redundant feature values and to maximize the face recognition performance. The recognition performances of the proposed feature set with a support vector machine-based classifier on three accessible and well-known 3D face databases, namely, Frav3D, Bosphorus, and Texas3D, show significant improvements over those achieved by state-of-the-art methods. This work also studies the feasibility of utilizing the component images in the complement component face space for data augmentation in convolutional neural network (CNN)-based frameworks.

A particle swarm optimization-based feature selection for unsupervised transfer learning

Transfer learning (TL) method has captured an attractive presence because it facilitates the learning ability in the target domain by acquiring knowledge from well-established source domains. To gain strong knowledge from the source domain, it is important to narrow down the distribution difference between the source and the target domains. For this purpose, it is necessary to consider the objectives such as preserving the discriminative information, preserving the original similarity of the source and the target domain data, maximizing the variance of the target domain, and preserving marginal and conditional distribution at the same time. Furthermore, some existing TL methods use only original feature data, so there is a threat of degenerated feature transformation. To overcome all these limitations, in this paper, a novel feature selection-based transfer learning approach using particle swarm optimization (PSO) for unsupervised transfer learning (FSUTL-PSO) is implemented. In FSUTL-PSO, we incorporate all such objectives into one fitness function and select common good features from the source and target domains based on the fitness function for eliminating the threat of degenerated features. Extensive experiments have been done on all possible tasks of Office+Caltech and PIE Face datasets and our proposed method FSUTL-PSO has shown significant improvement over the existing transfer or non-transfer learning methods.

Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble.

Coronary heart disease (CHD) is one of the severe health issues and is one of the most common types of heart diseases. It is the most frequent cause of mortality across the globe due to the lack of a healthy lifestyle. Owing to the fact that a heart attack occurs without any apparent symptoms, an intelligent detection method is inescapable. In this article, a new CHD detection method based on a machine learning technique, e.g., classifier ensembles, is dealt with. A two-tier ensemble is built, where some ensemble classifiers are exploited as base classifiers of another ensemble. A stacked architecture is designed to blend the class label prediction of three ensemble learners, i.e., random forest, gradient boosting machine, and extreme gradient boosting. The detection model is evaluated on multiple heart disease datasets, i.e., Z-Alizadeh Sani, Statlog, Cleveland, and Hungarian, corroborating the generalisability of the proposed model. A particle swarm optimization-based feature selection is carried out to choose the most significant feature set for each dataset. Finally, a two-fold statistical test is adopted to justify the hypothesis, demonstrating that the performance differences of classifiers do not rely upon an assumption. Our proposed method outperforms any base classifiers in the ensemble with respect to 10-fold cross validation. Our detection model has performed better than current existing models based on traditional classifier ensembles and individual classifiers in terms of accuracy, F1, and AUC. This study demonstrates that our proposed model adds a considerable contribution compared to the prior published studies in the current literature.

Multi-Objective Particle Swarm Optimization-based Feature Selection for Face Recognition

Multi-Objective Particle Swarm Optimization-based Feature Selection for Face Recognition

Particle Swarm Optimization based Feature Selection with Evolutionary Outlay-Aware Deep Belief Network Classifier (PSO-EOA-DBNC) for High Dimensional Datasets

Particle Swarm Optimization based Feature Selection with Evolutionary Outlay-Aware Deep Belief Network Classifier (PSO-EOA-DBNC) for High Dimensional Datasets

Apply Particle Swarm Optimization Algorithm to Measure the Software Quality

The process of improvement software quality began from early stages of software engineering development. It uses multiple quality metrics which are very important in software development. To calculate the standards quality of in software testing has been adopted. The software testing is focusing on the Software defect. In this paper is proposed new methods which combine the particle swarm optimization (PSO) to handle the best features Extraction with back-propagation networks to testing and evaluation of the data set . The paper depended database for NASA standards data. The result and experiment method improved quality performance for all classification methods used in the research"Combining Particle Swarm Optimization based Feature Selection and Bagging for Software Defect Prediction ".

An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing

Churn prediction in telecom has gained a huge prominence in the recent times due to the extensive interests exhibited by the stakeholders, large number of competitors and huge revenue losses incurred due to churn. Predicting telecom churn is challenging due to the voluminous and sparse nature of the data. This paper presents a technique for the telecom churn prediction that employs particle swarm optimization (PSO) and proposes three variants of PSO for churn prediction namely, PSO incorporated with feature selection as its pre-processing mechanism, PSO embedded with simulated annealing and finally PSO with a combination of both feature selection and simulated annealing. The proposed classifiers were compared with decision tree, naive bayes, K-nearest neighbor, support vector machine, random forest and three hybrid models to analyze their predictability levels and performance aspects. Accuracy, true positive rate, true negative rate, false positive rate, Precision, F-Measures, receiver operating characteristic and precision-recall plots were used as performance metrics. Experiments reveal that the performance of metaheuristics was more efficient and they also exhibited better predictability levels.

Image steganalysis using improved particle swarm optimization based feature selection

Image steganalysis is the task of discovering the hidden message in a multimedia file in which the steganalysis technique highly depends on the feature elements of the image. Since there is a possibility for a feature vector to contain redundant elements, processing of redundant elements can be harmful in terms of long computation cost and large storage space. This paper proposes a new feature selection approach based on Adaptive inertia weight-based Particle Swarm Optimization (APSO) for the image steganalysis where the inertia weight of PSO is adaptively adjusted using the swarm diameter, average distance of particles around the center and average speed of particles towards the center. Also, the proposed APSO is used with the novel measure of Area Under the receiver operating characteristics Curve (AUC) as the fitness function to enhance the performance of identification of stego-images from the cover images in steganalysis problem. Due to appropriate convergence rate and the regulated search step of APSO, it is able to select the most significant and influential feature elements and so, the performance of steganalysis will be improved. Experimental results of the proposed method on Breaking Out Steganography System (BOSS) benchmark proves the superiority of the proposed method compared to the similar approaches in image steganalysis in terms of detection of stego-image, running time, and diversity measure.

RGAP: A Rough Set, Genetic Algorithm and Particle Swarm Optimization based Feature Selection Approach

RGAP: A Rough Set, Genetic Algorithm and Particle Swarm Optimization based Feature Selection Approach

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Particle Swarm Optimization based Feature Selection

Selection is a pre-processing step in knowledge discovery from data (KDD) which aims at retrieving relevant data from the database beforehand. It imparts quality to the results of data mining tasks by selecting optimal feature set from larger set of features. Various feature selection techniques have been proposed in past which, unfortunately, suffer from unavoidable problems such as high computational cost and getting stuck into the local optima. Evolutionary algorithms such as Particle Swarm Optimization (PSO) possess immense abilities to explore a large search space and rarely fall into local optima thus making them a nice choice for feature selection. In this paper, we have explored pros and cons of traditional and PSO based feature selection techniques and suggested some effective changes in existing approaches. KeywordsSwarm Optimization (PSO), Evolutionary Algorithm

Particle swarm optimization-based feature selection in sentiment classification

Sentiment classification is one of the important tasks in text mining, which is to classify documents according to their opinion or sentiment. Documents in sentiment classification can be represented in the form of feature vectors, which are employed by machine learning algorithms to perform classification. For the feature vectors, the feature selection process is necessary. In this paper, we will propose a feature selection method called fitness proportionate selection binary particle swarm optimization (F-BPSO). Binary particle swarm optimization (BPSO) is the binary version of particle swam optimization and can be applied to feature selection domain. F-BPSO is a modification of BPSO and can overcome the problems of traditional BPSO including unreasonable update formula of velocity and lack of evaluation on every single feature. Then, some detailed changes are made on the original F-BPSO including using fitness sum instead of average fitness in the fitness proportionate selection step. The modified method is, thus, called fitness sum proportionate selection binary particle swarm optimization (FS-BPSO). Moreover, further modifications are made on the FS-BPSO method to make it more suitable for sentiment classification-oriented feature selection domain. The modified method is named as SCO-FS-BPSO where SCO stands for "sentiment classification-oriented". Experimental results show that in benchmark datasets original F-BPSO is superior to traditional BPSO in feature selection performance and FS-BPSO outperforms original F-BPSO. Besides, in sentiment classification domain, SCO-FS-BPSO which is modified specially for sentiment classification is superior to traditional feature selection methods on subjective consumer review datasets.