Optimal Feature Selection Process Research Articles

Deep ensemble of classifier for intrusion detection in WSN and improved attack mitigation process

SummaryAttacks have a significant negative impact on the wireless sensor network's network operations. They target to weaken the network layer, if they are not completely eradicated, the networks' ability to execute their desired function becomes collapsed. While intended to monitor such unexpected attacks, anomaly‐based intrusion detection systems (AIDS) have a high risk of false positives. This article intends to propose a Deep Ensemble Intrusion detection with a Self‐adaptive Sewing Training‐Based Optimization (DIED‐SASTO) model that includes the following steps: (a) preprocessing, (b) feature extraction, (c) optimal feature selection, and (d) detection. Initially, an improved class imbalance processing will take place to solve the imbalance problem during the preprocessing step. Subsequently, the features are extracted including higher‐order statistical features, improved entropy, and correlation‐based features during the feature extraction step. From the extracted feature set, as the curse of dimensionality becomes a serious challenge, it is significant to choose the optimal features. In the optimal feature selection process, a new Self‐adaptive Sewing Training‐Based Optimization (SASTO) is used. In addition, the intrusion detection will be employed based on the selected optimal features and trained using deep ensemble models like Deep maxout, Deep Belief Network (DBN), and Bidirectional Long short‐term memory (Bi‐LSTM). Once the presence of an attack is detected, it is mandatory to mitigate the attacker node form the network. For Attack Mitigation, improved entropy will be progressed. The effectiveness of the DIED‐SASTO is evaluated over conventional methods in terms of various metrics.

HEURISTIC-ASSISTED ADAPTIVE HYBRID DEEP LEARNING MODEL WITH FEATURE SELECTION FOR EPILEPSY DETECTION USING EEG SIGNALS

The word epilepsy is related to a neurological disease occurred by abnormalities of brain neurons. Timely detection of epilepsy is helpful for patients to decrease the mortality rate. To detect seizures, the Encephalogram (EEG) signals are analyzed based on monitoring the conditions of patients, and seizures can be detected from the EEG signal at appropriate times. The manual detection from the EEG signal requires more time for detecting the seizures and also it needs domain knowledge. The miss detection is eliminated by improving the classification performance in automatic epilepsy detection. Nowadays, deep learning models have not been greatly harnessed in the detection of epileptic seizures due to inappropriate descriptions of time-domain signals and sub-optimal classifier design. The aforementioned issues are combated by the novel Adaptive Hybrid Deep Learning (AHDL) approaches for epilepsy detection using EEG signals. Initially, the required EEG signal is collected from benchmark datasets. The collected signals are subjected to a signal decomposition phase that is accomplished by five levels of decomposition using Dual-Tree Complex Wavelet Transform (DTCWT), where the parameters are tuned by Improved Probability-based Coyote Optimization Algorithm (IP-COA). Further, the decomposed signal is given for feature extraction, where it divides the signal into two phases. In the first phase, the first feature set is obtained by using One-Dimensional Convolutional Neural Network (1DCNN), whereas in the second phase, the proposed model utilizes Auto Encoder (AE) to provide the second feature set. These resultant features are getting fused and the optimal feature selection process is found, where the features are obtained optimally by the IP-COA. Finally, epilepsy detection is accomplished with the aid of proposed AHDL with both Radial Basis-Recurrent Neural Networks (RB-RNN), where the hyperparameters are optimized using IP-COA. Thus, the experimental results illustrate that the suggested model enhances the detection and classification rate.

Enhanced Bird Swarm Algorithm with Deep Learning based Electroencephalography Signal Analysis for Emotion Recognition

Abstract Bioelectric signals comprise a massive count of data, and researchers in various domains containing cognitive neuroscience, psychiatry, and so on. Emotion is a vital part of regular human communication. The emotional conditions and dynamics of brain are connected by electroencephalography (EEG) signal which is utilized by Brain-Computer Interface (BCI), for providing optimum human-machine interaction. EEG-based emotion detection was extremely utilized in military, human-computer interactions, medicinal analysis, and other domains. Identifying emotions utilizing biological brain signals need accurate and effectual signal processing and extracting features approaches. But, one of the essential problems facing the emotion detection method, utilizing EEG signal is the detection accuracy. In this aspect, this study develops an Enhanced Bird Swarm Algorithm with Deep Learning based Electroencephalography Signal Analysis for Emotion Recognition (EBSADL-ESEG) technique. The ultimate aim of the EBSADL-ESEG technique lies in the recognition of emotions using the EEG signals accurately. To perform this, the EBSADL-ESEG technique initially extracts the statistical features from the EEG signals. In addition, the EBSA technique is employed for optimal feature selection process. Moreover, the gated recurrent unit (GRU) with root mean square propagation (RMSProp) optimizer is utilized for classifying distinct emotions (arousal, valence, and liking). The experimental analysis of the EBSADL-ESEG model is tested on DEAP dataset and the outcomes are investigated under diverse measures. The comprehensive comparison study revealed better outcomes of the EBSADL-ESEG model over other DL models.

Enhancement of email spam detection using improved deep learning algorithms for cyber security

Email has sustained to be an essential part of our lives and as a means for better communication on the internet. The challenge pertains to the spam emails residing a large amount of space and bandwidth. The defect of state-of-the-art spam filtering methods like misclassification of genuine emails as spam (false positives) is the rising challenge to the internet world. Depending on the classification techniques, literature provides various algorithms for the classification of email spam. This paper tactics to develop a novel spam detection model for improved cybersecurity. The proposed model involves several phases like dataset acquisition, feature extraction, optimal feature selection, and detection. Initially, the benchmark dataset of email is collected that involves both text and image datasets. Next, the feature extraction is performed using two sets of features like text features and visual features. In the text features, Term Frequency-Inverse Document Frequency (TF-IDF) is extracted. For the visual features, color correlogram and Gray-Level Co-occurrence Matrix (GLCM) are determined. Since the length of the extracted feature vector seems to the long, the optimal feature selection process is done. The optimal feature selection is performed by a new meta-heuristic algorithm called Fitness Oriented Levy Improvement-based Dragonfly Algorithm (FLI-DA). Once the optimal features are selected, the detection is performed by the hybrid learning technique that is composed of two deep learning approaches named Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). For improving the performance of existing deep learning approaches, the number of hidden neurons of RNN and CNN is optimized by the same FLI-DA. Finally, the optimized hybrid learning technique having CNN and RNN classifies the data into spam and ham. The experimental outcomes show the ability of the proposed method to perform the spam email classification based on improved deep learning.

A new variant of deep belief network assisted with optimal feature selection for heart disease diagnosis using IoT wearable medical devices

In this paper, the information related to heart disease using IoT wearable devices is collected from any benchmark site, which is publicly available. With the collected data, feature extraction process is performed initially, in which heart rate, zero crossing rate, and higher order statistical features like standard deviation, median, skewness, kurtosis, variance, mean, peak amplitude, and entropy are extracted. For acquiring most significant features, the optimal feature selection process is implemented. As a novel contribution, the feature selection process is done by the hybrid optimization algorithm called PS-GWO by integrating GWO and PSO. Next, the extracted features are subjected to a famous deep learning algorithm named modified DBN, in which the activation function and number of hidden neurons is optimized using the same developed hybrid algorithm to improve the heart diagnosis accuracy. From the analysis, for the test case 1, the accuracy of the developed PS-GWO-DBN is 60%, 52.5%, 35% and 35% increased than NN, KNN, SVM, and DBN. For test case 2, the accuracy of the proposed PS-GWO-DBN is 26%, 24%, 21.6% and 17% increased than NN, KNN, SVM, and DBN, respectively. The accuracy of the designed PS-GWO-DBN is 26% advanced than NN, 24% advanced than KNN, 21.6% advanced than SVM and 17% advanced than DBN for test case 3. Thus, the proposed heart disease prediction model using PS-GWO-DBN performs better than other classifiers.

Grey wolf assisted dragonfly-based weighted rule generation for predicting heart disease and breast cancer.

Disease prediction plays a significant role in the life of people, as predicting the threat of diseases is necessary for citizens to live life in a healthy manner. The current development of data mining schemes has offered several systems that concern on disease prediction. Even though the disease prediction system includes more advantages, there are still many challenges that might limit its realistic use, such as the efficiency of prediction and information protection. This paper intends to develop an improved disease prediction model, which includes three phases: Weighted Coalesce rule generation, Optimized feature extraction, and Classification. At first, Coalesce rule generation is carried out after data transformation that involves normalization and sequential labeling. Here, rule generation is done based on the weights (priority level) assigned for each attribute by the expert. The support of each rule is multiplied with the proposed weighted function, and the resultant weighted support is compared with the minimum support for selecting the rules. Further, the obtained rule is subject to the optimal feature selection process. The hybrid classifiers that merge Support Vector Machine (SVM), and Deep Belief Network (DBN) takes the role of classification, which characterizes whether the patient is affected with the disease or not. In fact, the optimized feature selection process depends on a new hybrid optimization algorithm by linking the Grey Wolf Optimization (GWO) with Dragonfly Algorithm (DA) and hence, the presented model is termed as Grey Wolf Levy Updated-DA (GWU-DA). Here, the heart disease and breast cancer data are taken, where the efficiency of the proposed model is validated by comparing over the state-of-the-art models. From the analysis, the proposed GWU-DA model for accuracy is 65.98 %, 53.61 %, 42.27 %, 35.05 %, 34.02 %, 11.34 %, 13.4 %, 10.31 %, 9.28 % and 9.89 % better than CBA + CPAR, MKL + ANFIS, RF + EA, WCBA, IQR + KNN + PSO, NL-DA + SVM + DBN, AWFS-RA, HCS-RFRS, ADS-SM-DNN and OSSVM-HGSA models at 60th learning percentage.

Optimal feature selection with hybrid classification for automatic face shape classification using fitness sorted Grey wolf update

Depending on the prevailing researches, the classification of face shapes could be deployed for numerous applications. This paper intends to develop a new method for face shape classification using intelligent approaches. The presented method includes three stages namely, (i) Face Detection (ii) Pre-processing (iii) Feature extraction (iv) Classification. The face detection process is used for identifying the most significant objects in the face, probably eyes, nose, etc. which is done using the Viola-Jones algorithm. Moreover, the pre-processing stage includes the Histogram Equalization (HE) model for enhancing the contrast of the image. The classification of face shapes is performed by a hybrid classifier that links Convolutional Neural Network (CNN) and Neural Network (NN). For performing the CNN-based classification, the images are directly given as input. On the other hand, NN-based classification requires features as input. Hence, the pre-processed image is again subjected to the feature extraction process, where the features are extracted using the Active Appearance Model (AAM) and the Active Shape Model (ASM). For reducing the length of extracted features, the optimal feature selection process is adopted, which is done by improved Grey Wolf Optimization (GWO) algorithm. As the main contribution, the features, number of hidden neurons in the convolutional layer of CNN, and training of NN (weight update) is optimally chosen by improved GWO so-called as Fitness Sorted Grey Wolf Update (FS-GU) model. Finally, the average of two outcomes from both CNN, and NN provides the classified five categories of face shapes like heart, oblong, oval, round, and square. The performance of the proposed classification model is finally validated by comparing over the conventional models by analyzing the relevant performance metrics.

Application of Feature Extraction Algorithm in the Construction of Interactive English Chinese Translation Mode

In the process of English translation, traditional interactive English translation system is not obvious in English semantic context. The optimal feature selection process does not achieve the optimal translation solution, and the translation accuracy is low. Based on this, this paper designs an interactive English Chinese translation system based on a feature extraction algorithm. By introducing the feature extraction algorithm, the optimal translation solution is selected, and the semantic mapping model is constructed to translate the best translation into English Chinese translation. The real experiment results show that the interactive English Chinese translation system based on feature extraction algorithm can get the best solution.

An efficient ALO-based ensemble classification algorithm for medical big data processing

In this paper, we indented to propose a consolidated feature selection and ensemble-based classification strategy to diminish the medical big data. Here, the proposed system will be the joint execution of both the ant lion optimiser (ALO) and ensemble classifier. So as to limit the impact of an imbalanced healthcare dataset, ALO is used for the optimal feature selection process. The optimised feature sets are classified by utilising the ensemble classification technique. The ensemble classification method uses the diversity of individual classification models to create better classification results. In this paper, the proposed ensemble classification algorithm used the support vector machine (SVM), and recurrent neural network (RNN) classifier and the results of every classifier were consolidated by the majority voting technique. It was watched that the proposed ensemble technique got promising classification accuracy contrasted and other ensemble strategies. This ensemble system can administer datasets, as quick as required giving the imperative help to viably perceive the underrepresented class. The proposed approach will diminish the big medical data precisely and productively. The simulation result shows that the proposed method has better classification when compared with the single classifiers such as random forest (RF), SVM and naïve Bayes classifier.

Optimal Feature Selection and Hybrid Classification for Autism Detection in Young Children

Abstract The early detection of autism spectrum disorder acts as a risk in the infants and toddlers as per the increase over the early invention awareness. Hence, this paper has made an effort to introduce a new autism detection technique in young children, which poses three major phases called weighted logarithmic transformation, optimal feature selection and classification. Initially, weighted transformation in the input data is carried out that correctly distinguishes the interclass labels, and it is determined to be the specified features. Because of the presence of numerous amounts of features, the ‘prediction’ becomes a serious issue, and therefore the optimal selection of features is important. Here, for optimal feature selection process, a new Levi Flight Cub Update-based lion algorithm (LFCU-LA) is introduced that can be a modification in lion algorithm. Once the optimal features are selected, they are given to the classification process that exploits a hybrid classifier: deep belief network (DBN) and neural network (NN). Additionally, the most important contributions in the hidden neurons of DBN and NN were optimally selected that paves way for exact detection.

An efficient ALO-based ensemble classification algorithm for medical big data processing

In this paper, we indented to propose a consolidated feature selection and ensemble-based classification strategy to diminish the medical big data. Here, the proposed system will be the joint execution of both the ant lion optimiser (ALO) and ensemble classifier. So as to limit the impact of an imbalanced healthcare dataset, ALO is used for the optimal feature selection process. The optimised feature sets are classified by utilising the ensemble classification technique. The ensemble classification method uses the diversity of individual classification models to create better classification results. In this paper, the proposed ensemble classification algorithm used the support vector machine (SVM), and recurrent neural network (RNN) classifier and the results of every classifier were consolidated by the majority voting technique. It was watched that the proposed ensemble technique got promising classification accuracy contrasted and other ensemble strategies. This ensemble system can administer datasets, as quick as required giving the imperative help to viably perceive the underrepresented class. The proposed approach will diminish the big medical data precisely and productively. The simulation result shows that the proposed method has better classification when compared with the single classifiers such as random forest (RF), SVM and naïve Bayes classifier.

RETRACTED ARTICLE: Optimal feature-based multi-kernel SVM approach for thyroid disease classification

Thyroid diseases are across the board around the world. In India as well, there is a critical issue caused because of this disease. Different research studies estimate that around 42 million individuals in India suffer from the ill effects of “thyroid diseases.” Diagnosis of health situations is an energetic and testing undertaking in the field of therapeutic science. Our proposed model is to classify this thyroid data utilizing optimal feature selection and kernel-based classifier process. We will create classifications models and its group show for classification of data using “multi kernel support vector machine.” The novelty and objective of this proposed model as feature selection, it’s used to enhance the performance of classifying process with the help of improved gray wolf optimization. Reason for this optimal feature selection as the insignificant features from unique dataset and computationally increment the performance of the model. The proposed thyroid classification results in the accuracy, sensitivity, and specificity of 97.49, 99.05 and 94.5%, compared to the existing model. This performance measure is computed from the confusion matrix with the diverse measures contrasted with individual models and in addition to the existing classifier and optimization techniques.

Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a “One Vs Rest” architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.

A survey of nature-inspired algorithms for feature selection to identify Parkinson's disease

Background and ObjectivesParkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Analyzing human gait serves to be useful in studies aiming at early recognition of the disease. In this paper we perform a comparative analysis of various nature inspired algorithms to select optimal features/variables required for aiding in the classification of affected patients from the rest. MethodsFor the experiments, we use a real life dataset of 166 people containing both healthy controls and affected people. Following the optimal feature selection process, the dataset is then classified using a neural network. Results and ConclusionsThe experimental results show Binary Bat Algorithm outperformed traditional techniques like Particle Swarm Optimization (PSO), Genetic Algorithm and Modified Cuckoo Search Algorithm with a competitive recognition rate on the dataset of selected features. We compare this through different criteria like cross-validated accuracies, true positive rates, false positive rates, positive predicted values and negative predicted values.

A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC

Membrane proteins play important roles in many biochemical processes and are also attractive targets of drug discovery for various diseases. The elucidation of membrane protein types provides clues for understanding the structure and function of proteins. Recently we developed a novel system for predicting protein subnuclear localizations. In this paper, we propose a simplified version of our system for predicting membrane protein types directly from primary protein structures, which incorporates amino acid classifications and physicochemical properties into a general form of pseudo-amino acid composition. In this simplified system, we will design a two-stage multi-class support vector machine combined with a two-step optimal feature selection process, which proves very effective in our experiments. The performance of the present method is evaluated on two benchmark datasets consisting of five types of membrane proteins. The overall accuracies of prediction for five types are 93.25% and 96.61% via the jackknife test and independent dataset test, respectively. These results indicate that our method is effective and valuable for predicting membrane protein types. A web server for the proposed method is available at http://www.juemengt.com/jcc/memty_page.php

Optimal Structural Health Monitoring Feature Selection via Minimized Performance Uncertainty

Power spectral measurements are very ubiquitous for their utility in generating structural health monitoring (SHM) features, because of their clear physical interpretation and easy computation through Fourier transform. In most SHM applications, optimal features are always desired to perform whatever level of assessment is required. Optimal in this sense refers to a measure of performance capability to enhance decision-making, because structural health monitoring inevitably involves, at some level, a hypothesis test: in the binary case, the question becomes are the features extracted from data derived from a baseline condition? (baseline can also mean linear, or any reference condition designated the null hypothesis) or ...from data derived from a different (test) condition. Inevitably, this decision involves stochastic data, as any such candidate feature is compromised by noise, which we may categorize as (i) operational and environmental, (ii) measurement, and (iii) computational/estimation. Regardless of source, this noise leads to the propagation of uncertainty from inception to final estimation of the feature; in all cases, the subsequent distribution of the features can lead to significant false positive (Type I) or false negative (Type II) errors in the classification of the features via the hypothesis test. Frequency domain approaches for SHM typically involve estimation of some form of transfer function, typically the usual frequency response function (FRF). Based upon the statistical modeling of the uncertainty of feature estimations, this paper evaluates the performance of two FRF-derived features, namely the dot-product difference (DPD) and Euclidian distance (ED), and statistical significance detection qualities are quantitatively compared. In each of the feature evaluations, the performance comparison is executed under the condition of best trade-off between sensitivity and specificity, adopting receiver operating characteristics as the performance indicator. Monte Carlo simulation and lab-scaled tests on plate-like structures are both implemented to validate the optimal feature selection process and demonstrate performance enhancement. The comparisons are facilitated through computation of receiver operating characteristics (ROCs), which are data-driven methods for comparing detection rates to error rates as a function of decision boundaries established between data distributions, independent of the actual underlying distribution.

Efficient subgraph search over large uncertain graphs

Retrieving graphs containing a query graph from a large graph database is a key task in many graph-based applications, including chemical compounds discovery, protein complex prediction, and structural pattern recognition. However, graph data handled by these applications is often noisy, incomplete, and inaccurate because of the way the data is produced. In this paper, we study subgraph queries over uncertain graphs. Specifically, we consider the problem of answering threshold-based probabilistic queries over a large uncertain graph database with the possible world semantics. We prove that problem is #P-complete, therefore, we adopt a filtering-and-verification strategy to speed up the search. In the filtering phase, we use a probabilistic inverted index, PIndex, based on subgraph features obtained by an optimal feature selection process. During the verification phase, we develop exact and bound algorithms to validate the remaining candidates. Extensive experimental results demonstrate the effectiveness of the proposed algorithms.