scholarly journals Enhancing EEG-Based Mental Stress State Recognition using an Improved Hybrid Feature Selection Algorithm

2021 ◽  
Author(s):  
ALA HAG ◽  
Dini Handayani ◽  
Maryam Altalhi ◽  
Thulasyammal Pillai ◽  
Teddy Mantoro ◽  
...  
Keyword(s):  
Feature Selection ◽  
Stress State ◽  
Vector Space ◽  
Frequency Domain ◽  
Mental Stress ◽  
State Of The Art ◽  
Feature Subset ◽  
State Recognition ◽  
Optimal Feature Subset ◽  
Optimal Feature

Mental stress state recognition using electroencephalogram (EEG) signals for real-life applications needs a conventional wearable device. This requires an efficient number of EEG channels and an optimal feature set. The main objective of the study is to identify an optimal feature subset that can best discriminate mental stress states while enhancing the overall performance. Thus, multi-domain feature extraction methods were employed, namely, time domain, frequency domain, time-frequency domain, and network connectivity features, to form a large feature vector space. To avoid the computational complexity of high dimensional space, a hybrid feature selection (FS) method of minimum Redundancy Maximum Relevance with Particle Swarm Optimization and Support Vector Machine (mRMR-PSO-SVM) is proposed to remove noise, redundant, and irrelevant features and keep the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art heuristic methods. The proposed model has significantly reduced the features vector space by an average of 70% in comparison to the state-of-the-art methods while significantly increasing overall detection performance.

scholarly journals Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8370
Author(s):  
Ala Hag ◽  
Dini Handayani ◽  
Maryam Altalhi ◽  
Thulasyammal Pillai ◽  
Teddy Mantoro ◽  
...  
Keyword(s):  
Feature Selection ◽  
Vector Space ◽  
Frequency Domain ◽  
Mental Stress ◽  
State Of The Art ◽  
The State ◽  
Support Vector ◽  
Feature Subset ◽  
Optimal Feature Subset ◽  
Optimal Feature

In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.

Modulation Recognition of Digital Multimedia Signal Based on Data Feature Selection

2017 ◽  
Vol 8 (3) ◽  
pp. 90-111 ◽  
Author(s):  
Hui Wang ◽  
Li Li Guo ◽  
Yun Lin
Keyword(s):  
Feature Selection ◽  
Information Entropy ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Digital Multimedia ◽  
Optimal Feature Subset ◽  
Optimal Feature

Automatic modulation recognition is very important for the receiver design in the broadband multimedia communication system, and the reasonable signal feature extraction and selection algorithm is the key technology of Digital multimedia signal recognition. In this paper, the information entropy is used to extract the single feature, which are power spectrum entropy, wavelet energy spectrum entropy, singular spectrum entropy and Renyi entropy. And then, the feature selection algorithm of distance measurement and Sequential Feature Selection(SFS) are presented to select the optimal feature subset. Finally, the BP neural network is used to classify the signal modulation. The simulation result shows that the four-different information entropy can be used to classify different signal modulation, and the feature selection algorithm is successfully used to choose the optimal feature subset and get the best performance.

scholarly journals Optimal Feature Subset Selection for Imbalanced Class Data using SMOTE and Binary ALO Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 344-349
Keyword(s):  
Feature Selection ◽  
Class Imbalance ◽  
Classification Performance ◽  
Selection Model ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Spatial Features ◽  
Imbalanced Classes ◽  
Optimal Feature Subset ◽  
Optimal Feature

Feature selection in multispectral high dimensional information is a hard labour machine learning problem because of the imbalanced classes present in the data. The existing Most of the feature selection schemes in the literature ignore the problem of class imbalance by choosing the features from the classes having more instances and avoiding significant features of the classes having less instances. In this paper, SMOTE concept is exploited to produce the required samples form minority classes. Feature selection model is formulated with the objective of reducing number of features with improved classification performance. This model is based on dimensionality reduction by opt for a subset of relevant spectral, textural and spatial features while eliminating the redundant features for the purpose of improved classification performance. Binary ALO is engaged to solve the feature selection model for optimal selection of features. The proposed ALO-SVM with wrapper concept is applied to each potential solution obtained during optimization step. The working of this methodology is tested on LANDSAT multispectral image.

scholarly journals Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data

2019 ◽  
Vol 8 (2) ◽  
pp. 910-916
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Mutation Operator ◽  
Knn Classifier ◽  
Optimal Feature Subset ◽  
Optimal Feature

The optimal feature subset selection over very high dimensional data is a vital issue. Even though the optimal features are selected, the classification of those selected features becomes a key complicated task. In order to handle these problems, a novel, Accelerated Simulated Annealing and Mutation Operator (ASAMO) feature selection algorithm is suggested in this work. For solving the classification problem, the Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC) problem is introduced. In FMCCSC, consistent subset is combined with the K-Nearest Neighbour (KNN) classifier known as FMCCSC-KNN classifier. The two data sets Dorothea and Madelon from UCI machine repository are experimented for optimal feature selection and classification. The experimental results substantiate the efficiency of proposed ASAMO with FMCCSC-KNN classifier compared to Particle Swarm Optimization (PSO) and Accelerated PSO feature selection algorithms.

scholarly journals A Novel Feature Selection Method Based on Maximum Likelihood Logistic Regression for Imbalanced Learning in Software Defect Prediction

2020 ◽  
Vol 17 (5) ◽  
pp. 721-730
Author(s):  
Kamal Bashir ◽  
Tianrui Li ◽  
Mahama Yahaya
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Feature Selection ◽  
Maximum Likelihood ◽  
Defect Prediction ◽  
Feature Subset ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Optimal Feature Subset ◽  
Optimal Feature

The most frequently used machine learning feature ranking approaches failed to present optimal feature subset for accurate prediction of defective software modules in out-of-sample data. Machine learning Feature Selection (FS) algorithms such as Chi-Square (CS), Information Gain (IG), Gain Ratio (GR), RelieF (RF) and Symmetric Uncertainty (SU) perform relatively poor at prediction, even after balancing class distribution in the training data. In this study, we propose a novel FS method based on the Maximum Likelihood Logistic Regression (MLLR). We apply this method on six software defect datasets in their sampled and unsampled forms to select useful features for classification in the context of Software Defect Prediction (SDP). The Support Vector Machine (SVM) and Random Forest (RaF) classifiers are applied on the FS subsets that are based on sampled and unsampled datasets. The performance of the models captured using Area Ander Receiver Operating Characteristics Curve (AUC) metrics are compared for all FS methods considered. The Analysis Of Variance (ANOVA) F-test results validate the superiority of the proposed method over all the FS techniques, both in sampled and unsampled data. The results confirm that the MLLR can be useful in selecting optimal feature subset for more accurate prediction of defective modules in software development process

scholarly journals Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection

2019 ◽  
Vol 29 (1) ◽  
pp. 1598-1610 ◽  
Author(s):  
Manosij Ghosh ◽  
Ritam Guha ◽  
Imran Alam ◽  
Priyank Lohariwal ◽  
Devesh Jalan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Combination Method ◽  
Sufficient Information ◽  
Feature Subset ◽  
Final Solution ◽  
Swarm Optimization ◽  
Intermediate Solution ◽  
Optimal Feature Subset ◽  
Optimal Feature ◽  
Weighted Combination

Abstract Feature selection (FS) is a technique which helps to find the most optimal feature subset to develop an efficient pattern recognition model under consideration. The use of genetic algorithm (GA) and particle swarm optimization (PSO) in the field of FS is profound. In this paper, we propose an insightful way to perform FS by amassing information from the candidate solutions produced by GA and PSO. Our aim is to combine the exploitation ability of GA with the exploration capacity of PSO. We name this new model as binary genetic swarm optimization (BGSO). The proposed method initially lets GA and PSO to run independently. To extract sufficient information from the feature subsets obtained by those, BGSO combines their results by an algorithm called average weighted combination method to produce an intermediate solution. Thereafter, a local search called sequential one-point flipping is applied to refine the intermediate solution further in order to generate the final solution. BGSO is applied on 20 popular UCI datasets. The results were obtained by two classifiers, namely, k nearest neighbors (KNN) and multi-layer perceptron (MLP). The overall results and comparisons show that the proposed method outperforms the constituent algorithms in 16 and 14 datasets using KNN and MLP, respectively, whereas among the constituent algorithms, GA is able to achieve the best classification accuracy for 2 and 7 datasets and PSO achieves best accuracy for 2 and 4 datasets, respectively, for the same set of classifiers. This proves the applicability and usefulness of the method in the domain of FS.

scholarly journals Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1899 ◽  
Author(s):  
Lin Lin ◽  
Lin Xue ◽  
Zhiqiang Hu ◽  
Nantian Huang
Keyword(s):  
Feature Selection ◽  
New England ◽  
Load Forecasting ◽  
Feature Subset ◽  
Single Model ◽  
Modular Model ◽  
Selection Step ◽  
Selection For ◽  
Optimal Feature Subset ◽  
Optimal Feature

To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering the calendar feature. Second, a feature selection combined with a predictor process was carried out to select the optimal feature for building a reliable predictor with respect to each hour. The final modular model consisted of 24 predictors with a respective optimal feature subset for day-ahead load forecasting. New England and Singapore load data were used to evaluate the effectiveness of the proposed method. The results indicated that the accuracy of the proposed modular model was higher than that of the traditional method. Furthermore, conducting a feature selection step when building a predictor improved the accuracy of load forecasting.

AN OPTIMAL FEATURE SUBSET SELECTION METHOD BASED ON DISTANCE DISCRIMINANT AND DISTRIBUTION OVERLAPPING

2009 ◽  
Vol 23 (08) ◽  
pp. 1577-1597 ◽  
Author(s):  
JIANNING LIANG ◽  
SU YANG ◽  
YUANYUAN WANG
Keyword(s):  
Feature Selection ◽  
Computational Cost ◽  
Difficult Problem ◽  
Exhaustive Search ◽  
Feature Subset Selection ◽  
Search Problem ◽  
Feature Subset ◽  
Ranking Problem ◽  
Optimal Feature Subset ◽  
Optimal Feature

The goal of feature selection is to search the optimal feature subset with respect to the evaluation function. Exhaustively searching all possible feature subsets requires high computational cost. The alternative suboptimal methods are more efficient and practical but they cannot promise globally optimal results. We propose a new feature selection algorithm based on distance discriminant and distribution overlapping (HFSDD) for continuous features, which overcomes the drawbacks of the exhaustive search approaches and those of the suboptimal methods. The proposed method is able to find the optimal feature subset without exhaustive search or Branch and Bound algorithm. The most difficult problem for optimal feature selection, the search problem, is converted into a feature ranking problem following rigorous theoretical proof such that the computational complexity can be greatly reduced. Since the distribution of overlapping degrees between every two classes can provide useful information for feature selection, HFSDD also takes them into account by using a new approach to estimate the overlapping degrees. In this sense, HFSDD is a distance discriminant and distribution overlapping based solution. HFSDD was compared with ReliefF and mrmrMID on ten data sets. The experimental results show that HFSDD outperforms the other methods.

scholarly journals Gradient-Based Multi-Objective Feature Selection for Gait Mode Recognition of Transfemoral Amputees

2018 ◽  
Author(s):  
Gholamreza Khademi ◽  
Hanieh Mohammadi ◽  
Dan Simon
Keyword(s):  
Feature Selection ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
User Intent ◽  
Multi Objective ◽  
Mode Recognition ◽  
Gradient Based ◽  
Transfemoral Amputees ◽  
Optimal Feature Subset ◽  
Optimal Feature

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14% ± 1.51% and 98.45% ± 1.22% with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

scholarly journals iAIPs: Identifying Anti-Inflammatory Peptides Using Random Forest

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongxu Zhao ◽  
Zhixia Teng ◽  
Yanjuan Li ◽  
Dong Chen
Keyword(s):  
Feature Selection ◽  
Amino Acid ◽  
Random Forest ◽  
Feature Selection Method ◽  
Selection Strategy ◽  
Feature Subset ◽  
Evolutionary Analysis ◽  
Anti Inflammatory ◽  
Optimal Feature Subset ◽  
Optimal Feature

Recently, several anti-inflammatory peptides (AIPs) have been found in the process of the inflammatory response, and these peptides have been used to treat some inflammatory and autoimmune diseases. Therefore, identifying AIPs accurately from a given amino acid sequences is critical for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics and the acceleration of their application in therapy. In this paper, a random forest-based model called iAIPs for identifying AIPs is proposed. First, the original samples were encoded with three feature extraction methods, including g-gap dipeptide composition (GDC), dipeptide deviation from the expected mean (DDE), and amino acid composition (AAC). Second, the optimal feature subset is generated by a two-step feature selection method, in which the feature is ranked by the analysis of variance (ANOVA) method, and the optimal feature subset is generated by the incremental feature selection strategy. Finally, the optimal feature subset is inputted into the random forest classifier, and the identification model is constructed. Experiment results showed that iAIPs achieved an AUC value of 0.822 on an independent test dataset, which indicated that our proposed model has better performance than the existing methods. Furthermore, the extraction of features for peptide sequences provides the basis for evolutionary analysis. The study of peptide identification is helpful to understand the diversity of species and analyze the evolutionary history of species.

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