scholarly journals Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1447
Author(s):  
Pan Huang ◽  
Yanping Li ◽  
Xiaoyi Lv ◽  
Wen Chen ◽  
Shuxian Liu
Keyword(s):  
Feature Selection ◽  
Action Recognition ◽  
Nearest Neighbor ◽  
Health Indicators ◽  
Support Vector ◽  
Normal Walking ◽  
K Nearest Neighbor ◽  
Recognition Algorithms ◽  
Medical Health ◽  
Improved Algorithm

Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF.

A Comparison of the Analysis of Methods for Feature Extraction and Classification by Wavelet Transform in SSVEP BCIs

2020 ◽  
Author(s):  
Hoda Heidari ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Hamidreza Hosseinzadeh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Iir Filters ◽  
Wide Range ◽  
New Feature

Abstract Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods such as feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) (bank of filters, narrow-bank IIR filters, and wavelet transform magnitude), feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), and classification step applying k nearest neighbor (k-NN), perceptron, support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down.

TOWARDS AN AUTOMATIC DIAGNOSIS SYSTEM FOR LUMBAR DISC HERNIATION: THE SIGNIFICANCE OF LOCAL SUBSET FEATURE SELECTION

2018 ◽  
Vol 30 (06) ◽  
pp. 1850044 ◽  
Author(s):  
Elias Ebrahimzadeh ◽  
Farahnaz Fayaz ◽  
Mehran Nikravan ◽  
Fereshteh Ahmadi ◽  
Mohammadjavad Rahimi Dolatabad
Keyword(s):  
Feature Selection ◽  
Lumbar Disc Herniation ◽  
Disc Herniation ◽  
Nearest Neighbor ◽  
Lumbar Disc ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Daily Lives ◽  
Automatic Diagnosis ◽  
Cad System

Herniation in the lumbar area is one of the most common diseases which results in lower back pain (LBP) causing discomfort and inconvenience in the patients’ daily lives. A computer aided diagnosis (CAD) system can be of immense benefit as it generates diagnostic results within a short time while increasing precision of diagnosis and eliminating human errors. We have proposed a new method for automatic diagnosis of lumbar disc herniation based on clinical MRI data. We use T2-W sagittal and myelograph images. The presented method has been applied on 30 clinical cases, each containing 7 discs (210 lumbar discs) for the herniation diagnosis. We employ Otsu thresholding method to extract the spinal cord from MR images of lumbar disc. A third order polynomial is then aligned on the extracted spinal cords, and by the end of preprocessing stage, all the T2-W sagittal images will have been prepared for specifying disc boundary and labeling. Having extracted an ROI for each disc, we proceed to use intensity and shape features for classification. The extracted features have been selected by Local Subset Feature Selection. The results demonstrated 91.90%, 92.38% and 95.23% accuracy for artificial neural network, K-nearest neighbor and support vector machine (SVM) classifiers respectively, indicating the superiority of the proposed method to those mentioned in similar studies.

Feature Selection Algorithm for Hyperlipidemia Classification

2014 ◽  
Vol 701-702 ◽  
pp. 110-113
Author(s):  
Qi Rui Zhang ◽  
He Xian Wang ◽  
Jiang Wei Qin
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Information Gain ◽  
Classification Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Document Frequency ◽  
Selection Algorithms ◽  
Term Weights

This paper reports a comparative study of feature selection algorithms on a hyperlipimedia data set. Three methods of feature selection were evaluated, including document frequency (DF), information gain (IG) and aχ2 statistic (CHI). The classification systems use a vector to represent a document and use tfidfie (term frequency, inverted document frequency, and inverted entropy) to compute term weights. In order to compare the effectives of feature selection, we used three classification methods: Naïve Bayes (NB), k Nearest Neighbor (kNN) and Support Vector Machines (SVM). The experimental results show that IG and CHI outperform significantly DF, and SVM and NB is more effective than KNN when macro-averagingF1 measure is used. DF is suitable for the task of large text classification.

scholarly journals Product Review Based Customer Sentiment Analysis using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Optimization Algorithm ◽  
Nearest Neighbor ◽  
Hybrid Approach ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Feature Selection Technique ◽  
Feature Selection Problem

This research presents a way of feature selection problem for classification of sentiments that use ensemble-based classifier. This includes a hybrid approach of minimum redundancy and maximum relevance (mRMR) technique and Forest Optimization Algorithm (FOA) (i.e. mRMR-FOA) based feature selection. Before applying the FOA on sentiment analysis, it has been used as feature selection technique applied on 10 different classification datasets publically available on UCI machine learning repository. The classifiers for example k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and Naïve Bayes used the ensemble based algorithm for available datasets. The mRMR-FOA uses the Blitzer’s dataset (customer reviews on electronic products survey) to select the significant features. The classification of sentiments has noticed to improve by 12 to 18%. The evaluated results are further enhanced by the ensemble of k-NN, NB and SVM with an accuracy of 88.47% for the classification of sentiment analysis task.

A comparison of the analysis of methods for feature extraction and classification by Wavelet transform in SSVEP BCIs

2021 ◽  
Author(s):  
Hoda Heidari ◽  
zahra einalou ◽  
Mehrdad Dadgostar ◽  
Hamidreza Hosseinzadeh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Vector Machines ◽  
New Feature

Abstract Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods which includes 1) feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) and wavelet transform magnitude, 2) feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), 3) classification step applying k nearest neighbor (k-NN), support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down. The highest level of accuracy was obtained 91.39%.

scholarly journals Feature selection for human membrane protein type classification using filter methods

2019 ◽  
Vol 8 (4) ◽  
pp. 375
Author(s):  
Glenda Anak Kaya ◽  
Nor Ashikin Mohamad Kamal
Keyword(s):  
Feature Selection ◽  
Membrane Protein ◽  
Nearest Neighbor ◽  
Protein Sequences ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Specificity And Sensitivity ◽  
Filter Methods ◽  
Correlation Based Feature Selection ◽  
Membrane Protein Type

<span lang="EN-US">As the number of protein sequences in the database is increasing, effective and efficient techniques are needed to make these data meaningful.  These protein sequences contain redundant and irrelevant features that cause lower classification accuracy and increase the running time of the computational algorithm. In this paper, we select the best features using Minimum Redundancy Maximum Relevance(mRMR) and Correlation-based feature selection(CFS) methods. Two datasets of human membrane protein are used, S1 and S2.  After the features have been selected by mRMR and CFS, K-Nearest Neighbor(KNN) and Support Vector Machine(SVM) classifiers are used to classify these membrane proteins. The performance of these techniques is measured using accuracy, specificity and sensitivity. and F-measure. The proposed algorithm managed to achieve 76% accuracy for S1 and 73% accuracy for S2. Finally, our proposed methods present competitive results when compared with the previous works on membrane protein classification</span><span>.</span>

scholarly journals Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

2017 ◽  
Author(s):  
Nor Aziyatul Izni Mohd Rosli ◽  
Mohd Azizi Abdul Rahman ◽  
Malarvili Balakrishnan ◽  
Takashi Komeda ◽  
Saiful Amri Mazlan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Support Vector ◽  
Gender Classification ◽  
Gender Recognition ◽  
K Nearest Neighbor ◽  
Combinatorial Fusion ◽  
Feature Selection Approach ◽  
Fusion Approach

Gender recognition is trivial for physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during the stepping exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SMO). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Nazarloo's work (90.34%) and other classifiers.

scholarly journals Comparative between optimization feature selection by using classifiers algorithms on spam email

2019 ◽  
Vol 9 (6) ◽  
pp. 5479
Author(s):  
Ghada Rawashdeh ◽  
Rabiei Mamat ◽  
Zuriana Binti Abu Bakar ◽  
Noor Hafhizah Abd Rahim
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Harmony Search ◽  
High Growth ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Spam Filter ◽  
Simulating Annealing

<span lang="EN-US">Spam mail has become a rising phenomenon in a world that has recently witnessed high growth in the volume of emails. This indicates the need to develop an effective spam filter. At the present time, Classification algorithms for text mining are used for the classification of emails. This paper provides a description and evaluation of the effectiveness of three popular classifiers using optimization feature selections, such as Genetic algorithm, Harmony search, practical swarm optimization, and simulating annealing. The research focuses on a comparison of the effect of classifiers using K-nearest Neighbor (KNN), Naïve Bayesian (NB), and Support Vector Machine (SVM) on spam classifiers (without using feature selection) also enhances the reliability of feature selection by proposing optimization feature selection to reduce number of features that are not important.</span>

scholarly journals Predicting heart failure using a wrapper-based feature selection

2021 ◽  
Vol 21 (3) ◽  
pp. 1530
Author(s):  
Minh Tuan Le ◽  
Minh Thanh Vo ◽  
Nhat Tan Pham ◽  
Son V.T Dao
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Medical Practitioners ◽  
Machine Learning Model ◽  
Heart Contraction ◽  
Artificial Neural Network Ann

In the current health system, it is very difficult for medical practitioners/physicians to diagnose the effectiveness of heart contraction. In this research, we proposed a machine learning model to predict heart contraction using an artificial neural network (ANN). We also proposed a novel wrapper-based feature selection utilizing a grey wolf optimization (GWO) to reduce the number of required input attributes. In this work, we compared the results achieved using our method and several conventional machine learning algorithms approaches such as support vector machine, decision tree, K-nearest neighbor, naïve bayes, random forest, and logistic regression. Computational results show not only that much fewer features are needed, but also higher prediction accuracy can be achieved around 87%. This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.

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