Statistical Feature Selection Approach for Classification of Emotions From Speech

2020 ◽  
Author(s):  
Nilima Salankar ◽  
Anjali Mishra
Keyword(s):  
Feature Selection ◽  
Statistical Feature ◽  
Selection Approach ◽  
Feature Selection Approach

scholarly journals An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals

2021 ◽  
Vol 11 (15) ◽  
pp. 6983
Author(s):  
Maritza Mera-Gaona ◽  
Diego M. López ◽  
Rubiel Vargas-Canas
Keyword(s):  
Feature Selection ◽  
Sensitivity And Specificity ◽  
Eeg Signals ◽  
Specificity And Sensitivity ◽  
Selection Approach ◽  
Feature Selection Approach ◽  
The Stability ◽  
Selection Algorithms ◽  
Epileptiform Events

Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals to support the diagnosis of neurological pathologies, the current challenge is to improve the reliability of the tools to classify or detect abnormalities. In this study, we used an ensemble feature selection approach to integrate the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals. Discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and Support Vecctor Machine (SVM); furthermore, performance was assessed by accuracy, specificity, and sensitivity metrics. The evaluation results showed that Ensemble Feature Selection (EFS) is a helpful tool to select relevant features from the EEGs. Thus, the stability calculated for the EFS method proposed was almost perfect in most of the cases evaluated. Moreover, the assessed classifiers evidenced that the models improved in performance when trained with the EFS approach’s features. In addition, the classifier of epileptiform events built using the features selected by the EFS method achieved an accuracy, sensitivity, and specificity of 97.64%, 96.78%, and 97.95%, respectively; finally, the stability of the EFS method evidenced a reliable subset of relevant features. Moreover, the accuracy, sensitivity, and specificity of the EEG detector are equal to or greater than the values reported in the literature.

scholarly journals A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder

2020 ◽  
Vol 10 (10) ◽  
pp. 754
Author(s):  
Naseer Ahmed Khan ◽  
Samer Abdulateef Waheeb ◽  
Atif Riaz ◽  
Xuequn Shang
Keyword(s):  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Connectivity Matrix ◽  
Brain Disorder ◽  
Selection Approach ◽  
Feature Selection Approach

Autism disorder, generally known as Autism Spectrum Disorder (ASD) is a brain disorder characterized by lack of communication skills, social aloofness and repetitions in the actions in the patients, which is affecting millions of the people across the globe. Accurate identification of autistic patients is considered a challenging task in the domain of brain disorder science. To address this problem, we have proposed a three-stage feature selection approach for the classification of ASD on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI Dataset. In the first stage, a large neural network which we call a “Teacher ” was trained on the correlation-based connectivity matrix to learn the latent representation of the input. In the second stage an autoencoder which we call a “Student” autoencoder was given the task to learn those trained “Teacher” embeddings using the connectivity matrix input. Lastly, an SFFS-based algorithm was employed to select the subset of most discriminating features between the autistic and healthy controls. On the combined site data across 17 sites, we achieved the maximum 10-fold accuracy of 82% and for the individual site-wise data, based on 5-fold accuracy, our results outperformed other state of the art methods in 13 out of the total 17 site-wise comparisons.

A feature selection approach for automatic e-book classification based on discourse segmentation

2015 ◽  
Vol 49 (1) ◽  
pp. 2-22
Author(s):  
Jiunn-Liang Guo ◽  
Hei-Chia Wang ◽  
Ming-Way Lai
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Selection Process ◽  
Narrative Structure ◽  
Support Vector ◽  
Content Type ◽  
Digital Documents ◽  
Selection Approach ◽  
Feature Selection Approach

Purpose – The purpose of this paper is to develop a novel feature selection approach for automatic text classification of large digital documents – e-books of online library system. The main idea mainly aims on automatically identifying the discourse features in order to improving the feature selection process rather than focussing on the size of the corpus. Design/methodology/approach – The proposed framework intends to automatically identify the discourse segments within e-books and capture proper discourse subtopics that are cohesively expressed in discourse segments and treating these subtopics as informative and prominent features. The selected set of features is then used to train and perform the e-book classification task based on the support vector machine technique. Findings – The evaluation of the proposed framework shows that identifying discourse segments and capturing subtopic features leads to better performance, in comparison with two conventional feature selection techniques: TFIDF and mutual information. It also demonstrates that discourse features play important roles among textual features, especially for large documents such as e-books. Research limitations/implications – Automatically extracted subtopic features cannot be directly entered into FS process but requires control of the threshold. Practical implications – The proposed technique has demonstrated the promised application of using discourse analysis to enhance the classification of large digital documents – e-books as against to conventional techniques. Originality/value – A new FS technique is proposed which can inspect the narrative structure of large documents and it is new to the text classification domain. The other contribution is that it inspires the consideration of discourse information in future text analysis, by providing more evidences through evaluation of the results. The proposed system can be integrated into other library management systems.

scholarly journals An Improved Feature Selection Based on Effective Range for Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianzhong Wang ◽  
Shuang Zhou ◽  
Yugen Yi ◽  
Jun Kong
Keyword(s):  
Feature Selection ◽  
Gene Selection ◽  
State Of The Art ◽  
Feature Selection Method ◽  
Selection Method ◽  
Effective Range ◽  
Inclusion Relation ◽  
Statistical Feature ◽  
Selection Approach ◽  
Feature Selection Approach

Feature selection is a key issue in the domain of machine learning and related fields. The results of feature selection can directly affect the classifier’s classification accuracy and generalization performance. Recently, a statistical feature selection method named effective range based gene selection (ERGS) is proposed. However, ERGS only considers the overlapping area (OA) among effective ranges of each class for every feature; it fails to handle the problem of the inclusion relation of effective ranges. In order to overcome this limitation, a novel efficient statistical feature selection approach called improved feature selection based on effective range (IFSER) is proposed in this paper. In IFSER, an including area (IA) is introduced to characterize the inclusion relation of effective ranges. Moreover, the samples’ proportion for each feature of every class in both OA and IA is also taken into consideration. Therefore, IFSER outperforms the original ERGS and some other state-of-the-art algorithms. Experiments on several well-known databases are performed to demonstrate the effectiveness of the proposed method.

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