Efficient Feature Selection Research Articles

A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements

Financial statement fraud has increasingly become a serious problem for business, government, and investors. In fact, this threatens the reliability of capital markets, corporate heads, and even the audit profession. Auditors in particular face their apparent inability to detect large-scale fraud, and there are various ways to identify this problem. In order to identify this problem, the majority of the proposed methods are based on existing algorithms and have only attempted to identify human or simple data mining methods that have high overhead and are also costly. The data mining methods presented so far have had high computational overhead or low accuracy. The purpose of this study is to present a model in which an improved ID3 decision tree with a support vector machine is used as a hybrid approach and also to improve the performance and accuracy, genetic algorithm and multilayer perceptron neural networks are applied. More efficient feature selection has been used to reduce computational overhead. The tree proposed in the proposed method has the lowest depth possible and therefore has high velocity and low computational overhead. For this purpose, the financial statements of 151 listed companies in Tehran Stock Exchange during 2014-2015 were surveyed and 125 financial ratios were extracted using ANOVA test, 23 fraud related ratios were selected as model input data. The proposed model has a high accuracy of about 80% of prediction accuracy compared to similar models.

An efficient feature selection approach for detection of liver disease prediction

The human liver issue is a hereditary issue because of the habituality of liquor or impact by the infection. It can prompt liver disappointment or liver malignancy, if not been recognized in introductory stage. The point of the proposed strategy is to recognize the liver issue in beginning stage utilizing liver capacity test dataset. There are numerous problems of the liver that require clinical consideration by a doctor or other medical services proficient. The dataset contains a colossal volume of highlights estimations which are diminished using ReliefF based component assurance methodology. The dataset contains a gigantic rundown of abilities which is diminished using an improved segment decision procedure named as covering method. The current examination centers on different component determination methods, which is perhaps the most significant and every now and again utilized in information preprocessing for information mining. In this paper the impact of highlight determination on the exactness of Decision Tree and Naïve Bayes, classifiers is introduced utilizing Liver problem information. These two classifiers are contrasted and genuine dataset which are pre-prepared with highlight determination strategies. The proposed covering method depends on a choice tree - RelifF and credulous bayes – RelifF computation to pick the primary highlights from the given dataset. The picked subset of highlights then goes through a preprocessing step to introduce a consistency in the allotment of data. Since guileless bayes is seen to enjoy the benefit of giving a famous execution in portrayal stage.

Ensemble of LSTM Networks for Fault Detection, Classification, and Root Cause Identification in Quality Control Line

Industrial systems with multiple subsystems are monitored via various sensors to control the ongoing process. If the number of monitoring signals collected from these sensors is high and the number of faulty samples is low, then the machine learning methods may fail to provide effective solutions for fault detection and root cause identification. This paper proposes an efficient feature selection model based on the regularized LSTM neural networks, and fault detection and classification using an ensemble of binary LSTM classifiers. The model is verified in PHME Data Challenge 2021 which provides quality-control-pipeline monitoring data.

An Online Data-Driven Fault Diagnosis Method for Air Handling Units by Rule and Convolutional Neural Networks.

The stable operation of air handling units (AHU) is critical to ensure high efficiency and to extend the lifetime of the heating, ventilation, and air conditioning (HVAC) systems of buildings. In this paper, an online data-driven diagnosis method for AHU in an HVAC system is proposed and elaborated. The rule-based method can roughly detect the sensor condition by setting threshold values according to prior experience. Then, an efficient feature selection method using 1D convolutional neural networks (CNNs) is proposed for fault diagnosis of AHU in HVAC systems according to the system’s historical data obtained from the building management system. The new framework combines the rule-based method and CNNs-based method (RACNN) for sensor fault and complicated fault. The fault type of AHU can be accurately identified via the offline test results with an accuracy of 99.15% and fast online detection within 2 min. In the lab, the proposed RACNN method was validated on a real AHU system. The experimental results show that the proposed RACNN improves the performance of fault diagnosis.

Adaptive Salp Swarm Algorithm as Optimal Feature Selection for Power Quality Disturbance Classification

Power quality disturbance (PQD) is an influential situation that significantly declines the reliability of electrical distribution systems. Therefore, PQD classification is an important process for preventing system reliability degradation. This paper introduces a novel algorithm called “adaptive salp swarm algorithm (SSA)” as an optimal feature selection algorithm for PQD classification. Feature extraction and classifier of the proposed classification system were based on the discrete wavelet and the probabilistic neural network, respectively. The classification was focused on the 13 types of power quality signals. The optimal number of selected features for the proposed classification system was firstly determined. Then, it demonstrated that the optimally selected features resulted in the highest classification accuracy of 98.77%. High performance of the proposed classification system in the noisy environment, as well as based on the real dataset was also verified. Furthermore, the proposed SSA indicates a very high convergence rate compared to other well-known algorithms. A comparison of the proposed classification system’s performance to existing works was also carried out, revealing that the proposed system’s accuracy is on a high-range scale. Hence, the adaptive SSA becomes another efficient optimal feature selection algorithm for PQD classification.

A Feature Selection Method by using Chaotic Cuckoo Search Optimization Algorithm with Elitist Preservation and Uniform Mutation for Data Classification

Feature selection is an essential step in the preprocessing of data in pattern recognition and data mining. Nowadays, the feature selection problem as an optimization problem can be solved with nature-inspired algorithm. In this paper, we propose an efficient feature selection method based on the cuckoo search algorithm called CBCSEM. The proposed method avoids the premature convergence of traditional methods and the tendency to fall into local optima, and this efficient method is attributed to three aspects. Firstly, the chaotic map increases the diversity of the initialization of the algorithm and lays the foundation for its convergence. Then, the proposed two-population elite preservation strategy can find the attractive one of each generation and preserve it. Finally, Lévy flight is developed to update the position of a cuckoo, and the proposed uniform mutation strategy avoids the trouble that the search space is too large for the convergence of the algorithm due to Lévy flight and improves the algorithm exploitation ability. The experimental results on several real UCI datasets show that the proposed method is competitive in comparison with other feature selection algorithms.

CS-BPSO: Hybrid feature selection based on chi-square and binary PSO algorithm for Arabic email authorship analysis

Email authorship analysis is a challenging task involving the detection of an author’s style to help determine their identity. Emails represent a widespread application of big data, and email authorship analysis is widely performed in the forensic linguistics field. However, the high-dimensional feature space encountered in authorship analysis affects the classification performance. Moreover, the Arabic language is highly inflected and involves certain unique characteristics, which pose critical challenges in identifying the context. Therefore, the selection of prominent features is a critical step in realizing authorship analysis. Swarm intelligence (SI) algorithms are widely adopted to address such feature selection problems. In this study, an efficient hybrid feature selection algorithm based on binary particle swarm optimization (BPSO) and chi-square BPSO (CS-BPSO) was developed to enhance the performance of Arabic email authorship analysis. Static and dynamic features were considered. Experiments were conducted on Arabic email messages collected from a sample population to test the algorithm performance using three popular classifiers: support vector machine (SVM), K-nearest neighbour (KNN), and naïve Bayes (NB) classifiers. Different metrics, specifically, the accuracy, precision, recall, and f1-score, were considered as performance measures. The results showed that the CS-BPSO method achieves impressive results using dynamic features. The findings were quite satisfactory in terms of solving multiple types of difficulties, e.g., imbalanced dataset, small dataset, and short text.

Efficient feature selection analysis for accuracy malware classification

Android is designed for mobile devices and its open-source software. The growth and popularity of android platform are high compared to another platform. Due to its glory, the number of malware has been increasing exponentially. Android system used a permission mechanism to allow users and developers to manage their access to private information, system resources, and data storage required by Android applications (apps). It became an advantage to an attacker to violent the data. This paper proposes a novel framework for Android malware detection. Our framework used three major methods for effective feature representation on malware detection and used this method to classify malware and benign. The result demonstrates that the Random forest is with 23 features is more accurate detection than the other machine learning algorithm.

An Optimal Feature Selection Method for Automatic Face Retrieval Using Enhanced Grasshopper Optimization Algorithm

Background: Retrieval of facial images based on its contents is one of the main areas of research. However, images contain high dimensional feature vectors and it is a challenging task to select the relevant features due to the variations available in the images of similar objects. Therefore, the selection of relevant features is an important step to make the facial retrieval system computationally efficient and more accurate. Objective: The main aim of this paper is to design and develop an efficient feature selection method for obtaining relevant and non-redundant features from the face images so that the accuracy and computational cost of a face retrieval system can be improved. Methods: The proposed feature selection method uses a new enhanced grasshopper optimization algorithm to obtain the significant features from the high dimensional features vector of face images. The proposed algorithm modifies the target vector by considering more than one best solution which maintain the elitism property and save the search from local optimum. Furthermore, it has been utilized to select the prominent features from the high dimensional facial features vector. Results: The performance of the proposed feature selection method has been tested on Oracle Research Laboratory face database. The proposed method eliminates 89% features which are minimum among the other methods and increases the accuracy of face retrieval system to 96.5%. Conclusion: The enhanced grasshopper optimization algorithm-based feature selection method for face retrieval system outperforms the existing methods in terms of accuracy and computational cost.

Efficient feature selection technique for brain tumor classification utilizing hybrid fruit fly based abc and ann algorithm

In medical science, information technology plays a main role in image processing for medicinal investigation and classification. For this reason “Magnetic Resonance Imaging” (MRI) can be useful today in the medical world which in turn is used to generate a comprehensive picture of the body's soft tissue and bones. The suggested technique utilizes an effective technique for brain tumor segmentation and classification. To segment and classify the brain tumor MR image by Modified fuzzy c means clustering (MFCM) and Artificial Neural Network (ANN). From the input image the proposed method extracts the shape, intensity and texture features. Here the extracted features are optimally selected by means of Hybrid Fruit fly and artificial bee colony (HFFABC). Both the segmentation and classification performance of the implemented technique is estimated in terms of “Accuracy, Sensitivity, Specificity, Ppv, Npv, Fpr And Fnr”. The implementation result shows the efficiency of proposed tumor segmentation and classification technique in segmenting the tissues perfectly from the MRI images of human nervous system. The classification performance of the implemented techniques is evaluated in terms of accuracy, sensitivity and specificity. The implementation results show the sensitivity, specificity & efficiency as 98.1%, 99.8% and 99.59%respectively.

AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification.

Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient’s emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand emotions and can generate large feature vectors. In this article, we have proposed an efficient spatial feature extraction and feature selection method with a short processing time. The raw EEG signal is first divided into a smaller set of eigenmode functions called (IMF) using the empirical model-based decomposition proposed in our work, known as intensive multivariate empirical mode decomposition (iMEMD). The Spatio-temporal analysis is performed with Complex Continuous Wavelet Transform (CCWT) to collect all the information in the time and frequency domains. The multiple model extraction method uses three deep neural networks (DNNs) to extract features and dissect them together to have a combined feature vector. To overcome the computational curse, we propose a method of differential entropy and mutual information, which further reduces feature size by selecting high-quality features and pooling the k-means results to produce less dimensional qualitative feature vectors. The system seems complex, but once the network is trained with this model, real-time application testing and validation with good classification performance is fast. The proposed method for selecting attributes for benchmarking is validated with two publicly available data sets, SEED, and DEAP. This method is less expensive to calculate than more modern sentiment recognition methods, provides real-time sentiment analysis, and offers good classification accuracy.

Identification and Characterization of Species-Specific Severe Acute Respiratory Syndrome Coronavirus 2 Physicochemical Properties.

There is an urgent need to elucidate the underlying mechanisms of coronavirus disease (COVID-19) so that vaccines and treatments can be devised. Severe acute respiratory syndrome coronavirus 2 has genetic similarity with bats and pangolin viruses, but a comprehensive understanding of the functions of its proteins at the amino acid sequence level is lacking. A total of 4320 sequences of human and nonhuman coronaviruses was retrieved from the Global Initiative on Sharing All Influenza Data and the National Center for Biotechnology Information. This work proposes an optimization method COVID-Pred with an efficient feature selection algorithm to classify the species-specific coronaviruses based on physicochemical properties (PCPs) of their sequences. COVID-Pred identified a set of 11 PCPs using a support vector machine and achieved 10-fold cross-validation and test accuracies of 99.53% and 97.80%, respectively. These findings could provide key insights into understanding the driving forces during the course of infection and assist in developing effective therapies.

Efficient feature selection for logical analysis of large-scale multi-class datasets

Feature selection in logical analysis of data (LAD) can be cast into a set covering problem. In this paper, extending the results on feature selection for binary classification using LAD, we present a mathematical model that selects a minimum set of necessary features for multi-class datasets and develop a heuristic algorithm that is both memory and time efficient for this model correspondingly. The utility of the algorithm is illustrated on a small example and the superiority of our work is demonstrated through experiments on 6 real-life multi-class datasets from UCI repository.

Expectation propagation on the diluted Bayesian classifier.

Efficient feature selection from high-dimensional datasets is a very important challenge in many data-driven fields of science and engineering. We introduce a statistical mechanics inspired strategy that addresses the problem of sparse feature selection in the context of binary classification by leveraging a computational scheme known as expectation propagation (EP). The algorithm is used in order to train a continuous-weights perceptron learning a classification rule from a set of (possibly partly mislabeled) examples provided by a teacher perceptron with diluted continuous weights. We test the method in the Bayes optimal setting under a variety of conditions and compare it to other state-of-the-art algorithms based on message passing and on expectation maximization approximate inference schemes. Overall, our simulations show that EP is a robust and competitive algorithm in terms of variable selection properties, estimation accuracy, and computational complexity, especially when the student perceptron is trained from correlated patterns that prevent other iterative methods from converging. Furthermore, our numerical tests demonstrate that the algorithm is capable of learning online the unknown values of prior parameters, such as the dilution level of the weights of the teacher perceptron and the fraction of mislabeled examples, quite accurately. This is achieved by means of a simple maximum likelihood strategy that consists in minimizing the free energy associated with the EP algorithm.

Feature selection accelerated convolutional neural networks for visual tracking

Most of the existing tracking methods based on convolutional neural network (CNN) models are too slow for use in real-time applications despite their excellent tracking accuracy in comparison with traditional methods. Meanwhile, CNN tracking solutions are memory intensive and require considerable computational resources. In this paper, we propose a time-efficient and accurate tracking scheme, a feature selection accelerated CNN (FSNet) tracking solution based on MDNet (Multi-Domain Network). The large number of convolutional operations is a major contributor to the high computational cost of MDNet. To reduce the computational complexity, we incorporated an efficient mutual information-based feature selection over the convolutional layer that reduces the feature redundancy in feature maps. Considering that tracking is a typical binary classification problem, redundant feature maps can simply be pruned, which results in an insignificant influence on the tracking performance. To further accelerate the CNN tracking solution, a RoIAlign layer is added that can apply convolution to the entire image instead of just to each RoI (Region of Interest). The bilinear interpolation of RoIAlign could well reduce misalignment errors of the tracked target. In addition, a new fine-tuning strategy is used in the fully-connected layers to accelerate the online updating process. By combining the above strategies, the accelerated CNN achieves a speedup to 60 FPS (Frame Per Second) on the GPU compared with the original MDNet, which functioned at 1 FPS with a very low impact on tracking accuracy. We evaluated the proposed solution on four benchmarks: OTB50, OTB100 ,VOT2016 and UAV123. The extensive comparison results verify the superior performance of FSNet.

Battering Review Spam Through Ensemble Learning in Imbalanced Datasets

Abstract Nowadays, people’s buying or availing services decisions are subject to online available reviews/opinions. The authenticity of these reviews/opinions is dubious, as there exist many fake reviews posted to attain monetary benefits by promoting their own or demoting the competitor’s products or services known as review spam. Although the number of spam is relatively less than that of normal reviews in real-life, this class imbalance is a critical concern in review spam detection. The performance degrades when the classifier skew towards the majority class. Moreover, efficient feature selection is essentially needed for this issue. The purpose of this study is to develop a framework based on different effective feature selection along with data balancing techniques. Validation results show that our proposed framework commendably copes up with the review spam issue and a higher precision on the real-life dataset. Further, we tested the sensitivity of our proposed framework using both parametric and non-parametric tests and found it significant.

Power quality disturbance classification based on efficient adaptive Arrhenius artificial bee colony feature selection

Automatic power quality disturbance (PQD) classification is a challenging task for industry and utility. This work presents an algorithm for classifying the PQDs based on the combination of novel feature selection with machine learning (ML) techniques. In this work, discrete wavelet transform (DWT) is utilized to decompose the PQD signals and hence minimize the size of input vectors with multi-resolution analysis. Novel optimal feature selection-based classification is performed using probabilistic neural network and adaptive Arrhenius artificial bee colony (PNN- adaptive AABC) algorithm. The selection of optimal features may discard the redundant features and retain the useful features. Also, this adaptive AABC algorithm provides a higher convergence rate, and this is used for accurate feature selection since this work is applied for 16 PQD events. In other case, features extracted with DWT are processed and trained with various ML algorithms such as Decision Tree, support vector machine (SVM), and K-nearest neighbor (KNN). Finally, the comparison accuracy of the proposed classifier is compared with the existing classifiers. The proposed technique found to give improved results in most of the cases like accuracy, prediction speed, and training time, and the outcomes acquired as 99.98%, ~1260 obs/seconds, and 13.113 seconds, respectively.

Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector

Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model can be deployed for anomaly detection by using a comprehensive dataset with multiple attack types. Nowadays datasets contain many attributes. Such high dimensionality of datasets poses a significant challenge to information extraction in terms of time and space complexity. Moreover, having so many attributes may be a hindrance towards creation of a decision boundary due to noise in the dataset. Large scale data with redundant or insignificant features increases the computational time and often decreases goodness of fit which is a critical issue in cybersecurity. In this research, we have proposed and implemented an efficient feature selection algorithm to filter insignificant variables. Our proposed Dynamic Feature Selector (DFS) uses statistical analysis and feature importance tests to reduce model complexity and improve prediction accuracy. To evaluate DFS, we conducted experiments on two datasets used for cybersecurity research namely Network Security Laboratory (NSL-KDD) and University of New South Wales (UNSW-NB15). In the meta-learning stage, four algorithms were compared namely Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units, Random Forest and a proposed Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for accuracy estimation. For NSL-KDD, experiments revealed an increment in accuracy from 99.54% to 99.64% while reducing feature size of one-hot encoded features from 123 to 50. In UNSW-NB15 we observed an increase in accuracy from 90.98% to 92.46% while reducing feature size from 196 to 47. The proposed approach is thus able to achieve higher accuracy while significantly lowering number of features required for processing.

A Data-Driven Approach for Online Inter-Area Oscillatory Stability Assessment of Power Systems Based on Random Bits Forest Considering Feature Redundancy

To utilize the rapidly refreshed operating data of power systems fully and effectively, an integrated scheme for inter-area oscillatory stability assessment (OSA) is proposed in this paper using a compositive feature selection unit and random bits forest (RBF) algorithm. This scheme consists of offline, update, and online stages, and it can provide fast and accurate estimation of the oscillatory stability margin (OSM) by using the real-time system operating data. In this scheme, a compositive feature selection unit is specially designed to realize efficient feature selection, which can significantly reduce the data dimensionality, effectively alleviate feature redundancy, and provide accurate correlation information to system operators. Then, the feature set consisting of the selected pivotal features is used for the RBF training to build the mapping relationships between the OSM and the system operating variables. Moreover, to enhance the robustness of the scheme in the face of variable operating conditions, an update stage is developed. The effectiveness of the integrated scheme is verified on the IEEE 39-bus system and a larger 1648-bus system. Tests of estimation accuracy, data processing speed, and the impact of missing data and noise data on this scheme are implemented. Comparisons with other methods reveal the superiority of the integrated scheme. In addition, the robustness of the scheme to variations in system topology, distribution among generators and loads, and peak and minimum load is studied.

BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm.

Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.