scholarly journals Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2910
Author(s):  
Kei Suzuki ◽  
Tipporn Laohakangvalvit ◽  
Ryota Matsubara ◽  
Midori Sugaya
Keyword(s):  
Feature Selection ◽  
Single Channel ◽  
Feature Selection Method ◽  
Classification Model ◽  
Features Selection ◽  
Selection Methods ◽  
Emotion Classification ◽  
Model Based ◽  
Physiological Indexes ◽  
Emotion Estimation

In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications.

scholarly journals Enhanced Classification Method for Phishing Emails Detection

2020 ◽  
Vol 3 (1) ◽  
pp. 58-63
Author(s):  
Y. Mansour Mansour ◽  
Majed A. Alenizi
Keyword(s):  
Feature Selection ◽  
Information Gain ◽  
Hybrid Approach ◽  
Feature Selection Method ◽  
Search Space ◽  
Selection Method ◽  
Classification Model ◽  
Selection Methods ◽  
Accuracy Rate ◽  
Communication Method

Emails are currently the main communication method worldwide as it proven in its efficiency. Phishing emails in the other hand is one of the major threats which results in significant losses, estimated at billions of dollars. Phishing emails is a more dynamic problem, a struggle between the phishers and defenders where the phishers have more flexibility in manipulating the emails features and evading the anti-phishing techniques. Many solutions have been proposed to mitigate the phishing emails impact on the targeted sectors, but none have achieved 100% detection and accuracy. As phishing techniques are evolving, the solutions need to be evolved and generalized in order to mitigate as much as possible. This article presents a new emergent classification model based on hybrid feature selection method that combines two common feature selection methods, Information Gain and Genetic Algorithm that keep only significant and high-quality features in the final classifier. The Proposed hybrid approach achieved 98.9% accuracy rate against phishing emails dataset comprising 8266 instances and results depict enhancement by almost 4%. Furthermore, the presented technique has contributed to reducing the search space by reducing the number of selected features.

The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

2016 ◽  
Vol 8 ◽  
pp. 5 ◽  
Author(s):  
Fatemeh Alighardashi ◽  
Mohammad Ali Zare Chahooki
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Machine Learning Algorithms ◽  
Fault Prediction ◽  
Filter Method ◽  
Selection Methods ◽  
Software Projects ◽  
Software Fault Prediction ◽  
Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

scholarly journals Semi-Skipping Layered Gated Unit and Efficient Network: Hybrid Deep Feature Selection Method for Edge Computing in EEG-Based Emotion Classification

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 13378-13389
Author(s):  
Muhammad Adeel Asghar ◽  
Muhammad Jamil Khan ◽  
Humayun Shahid ◽  
Mohammad Shorfuzzaman ◽  
Neal Naixue Xiong ◽  
...  
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Edge Computing ◽  
Emotion Classification ◽  
Deep Feature

Predicting the Severity of Bug Reports Based on Feature Selection

2018 ◽  
Vol 28 (04) ◽  
pp. 537-558 ◽  
Author(s):  
Wenjie Liu ◽  
Shanshan Wang ◽  
Xin Chen ◽  
He Jiang
Keyword(s):  
Feature Selection ◽  
Software Maintenance ◽  
Feature Selection Method ◽  
Selection Methods ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Bug Reports ◽  
Single Feature ◽  
Bug Report ◽  
Severity Prediction

In software maintenance process, it is a fairly important activity to predict the severity of bug reports. However, manually identifying the severity of bug reports is a tedious and time-consuming task. So developing automatic judgment methods for predicting the severity of bug reports has become an urgent demand. In general, a bug report contains a lot of descriptive natural language texts, thus resulting in a high-dimensional feature set which poses serious challenges to traditionally automatic methods. Therefore, we attempt to use automatic feature selection methods to improve the performance of the severity prediction of bug reports. In this paper, we introduce a ranking-based strategy to improve existing feature selection algorithms and propose an ensemble feature selection algorithm by combining existing ones. In order to verify the performance of our method, we run experiments over the bug reports of Eclipse and Mozilla and conduct comparisons with eight commonly used feature selection methods. The experiment results show that the ranking-based strategy can effectively improve the performance of the severity prediction of bug reports by up to 54.76% on average in terms of [Formula: see text]-measure, and it also can significantly reduce the dimension of the feature set. Meanwhile, the ensemble feature selection method can get better results than a single feature selection algorithm.

Feature Selection Method for Hydraulic System Faults Diagnosis Based on GA-PLS

2010 ◽  
Vol 44-47 ◽  
pp. 1130-1134
Author(s):  
Sheng Li ◽  
Pei Lin Zhang ◽  
Bing Li
Keyword(s):  
Feature Selection ◽  
Hydraulic System ◽  
Nearest Neighbor ◽  
Feature Selection Method ◽  
Original Data ◽  
Selection Method ◽  
Classification Model ◽  
K Nearest Neighbor ◽  
K Nearest Neighbor Algorithm ◽  
Faults Diagnosis

Feature selection is a key step in hydraulic system fault diagnosis. Some of the collected features are unrelated to classification model, and some are high correlated to other features. These features are harmful for establishing classification model. In order to solve this problem, genetic algorithm-partial least squares (GA-PLS) is proposed for selecting the representative and optimal features. K nearest neighbor algorithm (KNN) is used for diagnosing and classifying hydraulic system faults. For expressing better performance of GA-PLS, the original data of a model engineering hydraulic system is used, and the results of GA-PLS are compared with all feature used and GA. The experimental results show that, the proposed feature method can diagnose and classify hydraulic system faults more efficiently with using fewer features.

scholarly journals A Machine Learning Approach to EEG-based Prediction of Human Affective States Using Recursive Feature Elimination Method

2021 ◽  
Vol 335 ◽  
pp. 04001
Author(s):  
Didar Dadebayev ◽  
Goh Wei Wei ◽  
Tan Ee Xion
Keyword(s):  
Feature Selection ◽  
Fractal Dimension ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Feature Selection Method ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Emotional States ◽  
Affective States ◽  
Emotion Classification

Emotion recognition, as a branch of affective computing, has attracted great attention in the last decades as it can enable more natural brain-computer interface systems. Electroencephalography (EEG) has proven to be an effective modality for emotion recognition, with which user affective states can be tracked and recorded, especially for primitive emotional events such as arousal and valence. Although brain signals have been shown to correlate with emotional states, the effectiveness of proposed models is somewhat limited. The challenge is improving accuracy, while appropriate extraction of valuable features might be a key to success. This study proposes a framework based on incorporating fractal dimension features and recursive feature elimination approach to enhance the accuracy of EEG-based emotion recognition. The fractal dimension and spectrum-based features to be extracted and used for more accurate emotional state recognition. Recursive Feature Elimination will be used as a feature selection method, whereas the classification of emotions will be performed by the Support Vector Machine (SVM) algorithm. The proposed framework will be tested with a widely used public database, and results are expected to demonstrate higher accuracy and robustness compared to other studies. The contributions of this study are primarily about the improvement of the EEG-based emotion classification accuracy. There is a potential restriction of how generic the results can be as different EEG dataset might yield different results for the same framework. Therefore, experimenting with different EEG dataset and testing alternative feature selection schemes can be very interesting for future work.

scholarly journals New Hybrid Features Selection Method: A Case Study on Websites Phishing

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Khairan D. Rajab
Keyword(s):  
Feature Selection ◽  
Detection Rate ◽  
Feature Selection Method ◽  
Selection Method ◽  
Decision Makers ◽  
Features Selection ◽  
Hybrid Features ◽  
Pros And Cons ◽  
New Feature

Phishing is one of the serious web threats that involves mimicking authenticated websites to deceive users in order to obtain their financial information. Phishing has caused financial damage to the different online stakeholders. It is massive in the magnitude of hundreds of millions; hence it is essential to minimize this risk. Classifying websites into “phishy” and legitimate types is a primary task in data mining that security experts and decision makers are hoping to improve particularly with respect to the detection rate and reliability of the results. One way to ensure the reliability of the results and to enhance performance is to identify a set of related features early on so the data dimensionality reduces and irrelevant features are discarded. To increase reliability of preprocessing, this article proposes a new feature selection method that combines the scores of multiple known methods to minimize discrepancies in feature selection results. The proposed method has been applied to the problem of website phishing classification to show its pros and cons in identifying relevant features. Results against a security dataset reveal that the proposed preprocessing method was able to derive new features datasets which when mined generate high competitive classifiers with reference to detection rate when compared to results obtained from other features selection methods.

scholarly journals The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4749
Author(s):  
Shaorong Zhang ◽  
Zhibin Zhu ◽  
Benxin Zhang ◽  
Bao Feng ◽  
Tianyou Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Extraction Methods ◽  
Extraction Time ◽  
Classification Model ◽  
Discrete Wavelet ◽  
New Feature

The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1–4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.

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